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Ezenarro J, Riu J, Ahmed HJ, Busto O, Giussani B, Boqué R. Measurement errors and implications for preprocessing in miniaturised near-infrared spectrometers: Classification of sweet and bitter almonds as a case of study. Talanta 2024; 276:126271. [PMID: 38761663 DOI: 10.1016/j.talanta.2024.126271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
Near-infrared (NIR) spectroscopy is a well-established analytical technique that has been used in many applications over the years. Due to the advancements in the semiconductor industry, NIR instruments have evolved from benchtop instruments to miniaturised portable devices. The miniaturised NIR instruments have gained more interest in recent years because of the fast and robust measurements they provide with almost no sample pretreatments. However, due to the very different configurations and characteristics of these instruments, they need a dedicated optimization of the measurement conditions, which is crucial for obtaining reliable results. To comprehensively grasp the capabilities and potentials offered by these sensors, it is imperative to examine errors that can affect the raw data, which is a facet frequently overlooked. In this study, measurement error covariance and correlation matrices were calculated and then visually inspected to gain insight into the error structures associated with the devices, and to find the optimal preprocessing technique that may result in the improvement of the models built. This strategy was applied to the classification of sweet and bitter almonds, which were measured with the three portable low-cost NIR devices (SCiO, FlameNIR+ and NeoSpectra Micro Development Kit) after removing the shelled, since their classification is of utmost importance for the almond industry. The results showed that bitter almonds can be classified from sweet almonds using any of the instruments after selecting the optimal preprocessing, obtained through inspection of covariance and correlation matrices. Measurements obtained with FlameNIR + device provided the best classification models with an accuracy of 98 %. The chosen strategy provides new insight into the performance characterization of the fast-growing miniaturised NIR instruments.
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Affiliation(s)
- Jokin Ezenarro
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Catalonia, Spain
| | - Jordi Riu
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Catalonia, Spain
| | - Hawbeer Jamal Ahmed
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Catalonia, Spain; United Science Colleges, Department of Chemistry, Bakhan 108, Sulaymaneyah, Iraq
| | - Olga Busto
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Catalonia, Spain
| | - Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Via Valleggio, 9, 22100, Como, Italy.
| | - Ricard Boqué
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Catalonia, Spain.
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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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Guerra A, De Marchi M, Niero G, Chiarin E, Manuelian CL. Application of a short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits. J Dairy Sci 2024; 107:3413-3419. [PMID: 38246541 DOI: 10.3168/jds.2023-24302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024]
Abstract
Portable handheld devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess the feasibility of using short-wave pocket-sized NIR devices to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740 to 1,070 nm. The reference data were matched with the corresponding spectrum and modified partial least squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual device performance and an external validation with 25% of the dataset as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R2ExV) and a residual predictive deviation (RPDExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R2ExV >0.70; RPDExV >1.73). However, poor prediction models were obtained for all the other traits with an R2ExV between 0.43 (urea) and 0.03 (SCC), and a RPDExV between 1.18 (urea) and 0.22 (SCC). In conclusion, short-wave portable handheld NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, SCC, urea, and freezing point.
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Affiliation(s)
- Alberto Guerra
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
| | - Giovanni Niero
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy.
| | - Elena Chiarin
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
| | - Carmen L Manuelian
- Group of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain
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Nantongo JS, Tinyiro SE, Nakitto M, Serunkuma E, Namugga P, Ayetigbo O, Mayanja S, Moyo M, Ssali R, Mendes T. End-user preferences to enhance prospects for varietal acceptance and adoption in potato breeding in Uganda. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4606-4614. [PMID: 37550770 DOI: 10.1002/jsfa.12882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/29/2023] [Accepted: 08/08/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Potato varieties have diverse biophysical characteristics, so it is important for breeders to have the capacity to choose those that meet the preferences of end users, such as mealiness, firmness, and taste, among others. Combining user preferences with descriptive information regarding the sensory characteristics of boiled potatoes can contribute to the improvement of consumer-driven varieties. This study aimed to factor in the preferences of end users to improve the prospects for varietal acceptance, adoption, and discrimination among genotypes in potato breeding. RESULTS The priority quality traits (traits that play the most significant roles in acceptance and adoption) of the boiled potatoes were determined by evaluating gender and livelihood using the G+ tool. The G+ tool is designed to assess gender impact on roots, tubers and bananas (RTB) traits by serving as a validation check to reflect on important gender-based issues in agricultural food systems in order to reduce harm and promote positive impact. Potato genotypes were differentiated by penetration (textural parameters as measured by standard texture probe) and the procedure was repeatable, as there was no significant difference between the cooking replicates at 40 min of cooking. Instrument-based texture parameters, such as penetration peak force (hardness/firmness) and area (area under the curve, which represents energy needed to penetrate) of boiled potato tubers were significantly associated with sensory attributes such as fracturability and hardness in the mouth. An attempt to differentiate genotypes using near-infrared spectroscopy (NIRS) revealed that the average results observed for the calibration for yellow color (r2 = 0.70), homogeneity of color (r2 = 0.48), moisture in mass (r2 = 0.40), and uniformity of texture (r2 = 0.56) suggested that these parameters could be used for initial breeding screening purposes. CONCLUSIONS The preferred traits of the boiled potato can be integrated into the potato-breeding program/product profile. Near-infrared spectroscopy shows strong potential to predict potato color and the ability of NIRS models to predict some texture attributes is also promising. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
| | | | | | | | - Prossy Namugga
- National Agriculture Research Laboratories, Kampala, Uganda
| | | | - Sarah Mayanja
- International Potato Center (CIP-SSA), Kampala, Uganda
| | - Mukani Moyo
- International Potato Center (CIP-SSA Regional Office), Nairobi, Kenya
| | - Reuben Ssali
- International Potato Center (CIP-SSA), Kampala, Uganda
| | - Thiago Mendes
- International Potato Center (CIP-SSA Regional Office), Nairobi, Kenya
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Cui H, Gu F, Qin J, Li Z, Zhang Y, Guo Q, Wang Q. Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach. Foods 2024; 13:1722. [PMID: 38890950 PMCID: PMC11171514 DOI: 10.3390/foods13111722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/21/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.
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Affiliation(s)
- Haofan Cui
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Fengying Gu
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Jingjing Qin
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Zhenyuan Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Yu Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Beijing 100081, China;
| | - Qin Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Qiang Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
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Muntean GC, Simedru D, Uiuiu P, Tanaselia C, Cadar O, Becze A, Coroian A. Evaluation of Alternative Sources of Proteins and Other Nutrients with Potential Applications in Fish Nutrition. Molecules 2024; 29:2332. [PMID: 38792193 PMCID: PMC11123814 DOI: 10.3390/molecules29102332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/25/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
The European Union's (EU) agricultural self-sufficiency is challenged by its reliance on imported plant proteins, particularly soy from the Americas, contributing to deforestation and greenhouse gas emissions. Addressing the EU's protein deficit, this study evaluates alternative protein sources for aquaculture, focusing on their nutritional value, elemental content, and polycyclic aromatic hydrocarbons (PAHs). Protein flours from gastropods (Helix pomatia, Arion lusitanicus, Arion vulgaris) and their hepatopancreas, along with plant-based proteins from food industry by-products (oilcakes, coffee grounds, spent brewer's yeast), were analyzed. Results revealed that snail flour contained the highest protein content at 59.09%, significantly outperforming hepatopancreas flour at 42.26%. Plant-based proteins demonstrated substantial nutritional value, with coffee grounds flour exhibiting a remarkable protein content of 71.8% and spent brewer's yeast flour at 57.9%. Elemental analysis indicated high levels of essential minerals such as magnesium in hepatopancreas flour (5719.10 mg/kg) and calcium in slug flour (48,640.11 mg/kg). However, cadmium levels in hepatopancreas flour (11.45 mg/kg) necessitate caution due to potential health risks. PAH concentrations were low across all samples, with the highest total PAH content observed in hepatopancreas flour at 0.0353 µg/kg, suggesting minimal risk of PAH-related toxicity. The analysis of plant-based protein sources, particularly oilcakes derived from sunflower, hemp, flax, and pumpkin seeds, revealed that these by-products not only exhibit high protein contents but present a promising avenue for enhancing the nutritional quality of feed. This study underscores the potential of utilizing gastropod and plant-based by-products as sustainable and nutritionally adequate alternatives to conventional feeds in aquaculture, contributing to the EU's environmental sustainability goals.
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Affiliation(s)
- George-Cătălin Muntean
- Department of Fundamental Sciences, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Mănăştur Street, RO-400372 Cluj-Napoca, Romania; (G.-C.M.); (P.U.); (A.C.)
| | - Dorina Simedru
- Research Institute for Analytical Instrumentation Subsidiary, National Institute for Research and Development of Optoelectronics Bucharest INOE 2000, 67 Donath Street, RO-400293 Cluj-Napoca, Romania; (D.S.); (C.T.); (O.C.)
| | - Paul Uiuiu
- Department of Fundamental Sciences, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Mănăştur Street, RO-400372 Cluj-Napoca, Romania; (G.-C.M.); (P.U.); (A.C.)
| | - Claudiu Tanaselia
- Research Institute for Analytical Instrumentation Subsidiary, National Institute for Research and Development of Optoelectronics Bucharest INOE 2000, 67 Donath Street, RO-400293 Cluj-Napoca, Romania; (D.S.); (C.T.); (O.C.)
| | - Oana Cadar
- Research Institute for Analytical Instrumentation Subsidiary, National Institute for Research and Development of Optoelectronics Bucharest INOE 2000, 67 Donath Street, RO-400293 Cluj-Napoca, Romania; (D.S.); (C.T.); (O.C.)
| | - Anca Becze
- Research Institute for Analytical Instrumentation Subsidiary, National Institute for Research and Development of Optoelectronics Bucharest INOE 2000, 67 Donath Street, RO-400293 Cluj-Napoca, Romania; (D.S.); (C.T.); (O.C.)
| | - Aurelia Coroian
- Department of Fundamental Sciences, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Mănăştur Street, RO-400372 Cluj-Napoca, Romania; (G.-C.M.); (P.U.); (A.C.)
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Peng C, Zhong L, Gao L, Li L, Nie L, Wu A, Huang R, Tian W, Yin W, Wang H, Miao Q, Zhang Y, Zang H. Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation. Int J Pharm 2024; 655:124001. [PMID: 38492896 DOI: 10.1016/j.ijpharm.2024.124001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/22/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.
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Affiliation(s)
- Cheng Peng
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Liang Zhong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Aoli Wu
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Ruiqi Huang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Weilu Tian
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
| | - Wenping Yin
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone, Zibo, Shandong 0533, China
| | - Hui Wang
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone, Zibo, Shandong 0533, China
| | - Qiyi Miao
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone, Zibo, Shandong 0533, China
| | - Yunshi Zhang
- Shandong SMA Pharmatech Co., Ltd, 165, Huabei Rd., High & New Technology Zone, Zibo, Shandong 0533, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, Shandong, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China.
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8
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Custodio-Mendoza JA, Aktaş H, Zalewska M, Wyrwisz J, Kurek MA. A Review of Quantitative and Topical Analysis of Anthocyanins in Food. Molecules 2024; 29:1735. [PMID: 38675555 PMCID: PMC11051960 DOI: 10.3390/molecules29081735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Anthocyanins, a subclass of flavonoids known for their vibrant colors and health-promoting properties, are pivotal in the nutritional science and food industry. This review article delves into the analytical methodologies for anthocyanin detection and quantification in food matrices, comparing quantitative and topical techniques. Quantitative methods, including High-performance Liquid Chromatography (HPLC) and Mass Spectrometry (MS), offer precise quantification and profiling of individual anthocyanins but require sample destruction, limiting their use in continuous quality control. Topical approaches, such as Near-infrared Spectroscopy (NIR) and hyperspectral imaging, provide rapid, in situ analysis without compromising sample integrity, ideal for on-site food quality assessment. The review highlights the advancements in chromatographic techniques, particularly Ultra-high-performance Liquid Chromatography (UHPLC) coupled with modern detectors, enhancing resolution and speed in anthocyanin analysis. It also emphasizes the growing importance of topical techniques in the food industry for their efficiency and minimal sample preparation. By examining the strengths and limitations of both analytical realms, this article aims to shed light on current challenges and prospective advancements, providing insights into future research directions for improving anthocyanin analysis in foods.
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Affiliation(s)
| | | | | | | | - Marcin A. Kurek
- Department of Technique and Food Development, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (WULS-SGGW), 02-776 Warsaw, Poland; (J.A.C.-M.); (H.A.); (M.Z.); (J.W.)
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9
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Fu H, Teng K, Shen Y, Zhao J, Qu H. Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123441. [PMID: 37748230 DOI: 10.1016/j.saa.2023.123441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 09/02/2023] [Accepted: 09/19/2023] [Indexed: 09/27/2023]
Abstract
Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.
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Affiliation(s)
- Hao Fu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Kaixuan Teng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Yunfei Shen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jie Zhao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China.
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10
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Cozzolino D, Chapman J. Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety. Anal Bioanal Chem 2024; 416:611-620. [PMID: 37542534 DOI: 10.1007/s00216-023-04849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 08/07/2023]
Abstract
Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.
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Affiliation(s)
- Daniel Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, St. Lucia, Brisbane, QLD, 4072, Australia.
| | - James Chapman
- School of Science, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia
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Jo E, Lee Y, Lee Y, Baek J, Kim JG. Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration. Food Chem Toxicol 2023; 181:114088. [PMID: 37804916 DOI: 10.1016/j.fct.2023.114088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/20/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023]
Abstract
The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for swiftly identifying counterfeit beef altered to appear fresh. The experiment involved 60 beef samples, half of which were artificially adulterated using a colouring solution. Despite meticulous analysis of the beef's colour attributes, no significant differences were observed between the fresh and adulterated samples. However, our method, utilising a 344-1040 nm spectral range, achieved a classification accuracy of 98.84%. To enhance practicality, we employed gradient-weighted class activation mapping and identified the 580-600 nm range as particularly influential for classification. Remarkably, even when we narrowed the input to the model to this spectral range, a high level of classification accuracy was maintained. To further validate the model's robustness and generalisability, we allocated 70 beef samples to an external validation set. Comparative performance analysis revealed that our model outperformed traditional machine learning algorithms, such as SVM and logistic regression, by 9.3% and 28.4%, respectively. Overall, this study offers invaluable insights for detecting counterfeited beef, thereby contributing to the preservation of meat product quality and integrity within the food industry.
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Affiliation(s)
- Eunjung Jo
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea; Department of Artificial Intelligence, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Youngjoo Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea
| | - Yumi Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea
| | - Jaewoo Baek
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea
| | - Jae Gwan Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea.
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12
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Cheng F, Yang C, Zhu H, Li Y, Lan L, Wang K. Semi-Supervised Deep Learning-Based Multi-component Spectral Calibration Modeling for UV-vis and Near-Infrared Spectroscopy without Information Loss. Anal Chem 2023; 95:13446-13455. [PMID: 37638661 DOI: 10.1021/acs.analchem.3c01132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Spectral analysis is an important method for characterizing and identifying chemical species. However, quantitative spectral analysis of multiple chemical properties in the real world has always been a challenging problem due to the strong correlation, massive noise, and serious information overlapping of the spectral features. Here, we present a new semi-supervised spectral calibration method based on information lossless decoupling of spectral features named NICEM. To realize the separation and extraction of key latent features, the method uses the flow-based model non-linear independent component estimation (NICE) to learn the sample distribution. The spectral data information is transformed into independent latent variables obeying Gaussian distribution by the reversible structure of deep network without information loss, so as to find the essential properties and realize the feature nonlinear decomposition. Moreover, the association between the input latent feature variables and attributes is evaluated by the maximum mutual information coefficient to eliminate the adverse effects of irrelevant information in the latent variable space and mine key information. Since the latent variables are independent in each dimension, the NICEM method is easier to establish an accurate semi-supervised multi-component calibration model even for high overlapping and complex spectral data. The applicability of the proposed spectral modeling method is demonstrated by using three ultraviolet-visible and near-infrared spectral data sets with 15 physical and chemical properties including diesel fuels, corn, and multi-metal ions solution. Results show that the proposed NICEM method has the highest determination coefficient (R2) and significantly improves extrapolation compared with the seven state-of-the-art methods. The proposed method is intuitive because it obviates complex feature engineering and prior knowledge and is a promising spectral calibration tool for quantitative analysis in other spectroscopy applications.
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Affiliation(s)
- Fei Cheng
- School of Automation, Central South University, Changsha 410083, China
| | - Chunhua Yang
- School of Automation, Central South University, Changsha 410083, China
| | - Hongqiu Zhu
- School of Automation, Central South University, Changsha 410083, China
| | - Yonggang Li
- School of Automation, Central South University, Changsha 410083, China
| | - Lijuan Lan
- School of Automation, Central South University, Changsha 410083, China
| | - Kai Wang
- School of Automation, Central South University, Changsha 410083, China
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13
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De Man A, Uyttersprot JS, Chavez PF, Vandenbroucke F, Bovart F, De Beer T. The application of Near-Infrared Spatially Resolved Spectroscopy in scope of achieving continuous real-time quality monitoring and control of tablets with challenging dimensions. Int J Pharm 2023; 641:123064. [PMID: 37211236 DOI: 10.1016/j.ijpharm.2023.123064] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 05/23/2023]
Abstract
In scope of achieving real-time release of tablets, quality attributes need to be monitored and controlled through Process Analytical Technology tools such as near-infrared spectroscopy (NIRS). The authors evaluated the suitability of NIR-Spatially Resolved Spectroscopy (NIR-SRS) for continuous real-time monitoring and control of content uniformity, hardness and homogeneity of tablets with challenging dimensions. A novel user-friendly research and development inspection unit was used as standalone equipment for the analysis of small oblong tablets with deep-cut break lines. A total of 66 tablets varying in hardness and Active Pharmaceutical Ingredient (API) content were inspected, with each tablet being analysed five times and measurements repeated on three different days. Partial Least Squares (PLS) models were developed to assess content uniformity and hardness, of which the former showed higher accuracy. The authors attempted to visualize tablet homogeneity through NIR-SRS spectra by regressing all spectra obtained during a single measurement using a content uniformity PLS model. The NIR-SRS probe demonstrated its potential towards real-time release testing through its ability to quickly monitor content uniformity, hardness and visualize homogeneity, even for tablets with challenging dimensions.
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Affiliation(s)
- A De Man
- Ghent University, Laboratory of Pharmaceutical Process Analytical Technology, Ottergemsesteenweg 460, 9000 Ghent, Belgium
| | - J-S Uyttersprot
- UCB Pharma, Pharma sciences, Chem. Du Foriest 1, 1420 Braine-l'Alleud, Belgium
| | - P-F Chavez
- UCB Pharma, Pharma sciences, Chem. Du Foriest 1, 1420 Braine-l'Alleud, Belgium
| | - F Vandenbroucke
- Pharma Technology, Rue Graham Bell 8, 1402 Thines (Nivelles), Belgium
| | - F Bovart
- Pharma Technology, Rue Graham Bell 8, 1402 Thines (Nivelles), Belgium
| | - T De Beer
- Ghent University, Laboratory of Pharmaceutical Process Analytical Technology, Ottergemsesteenweg 460, 9000 Ghent, Belgium
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14
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Teixido-Orries I, Molino F, Femenias A, Ramos AJ, Marín S. Quantification and classification of deoxynivalenol-contaminated oat samples by near-infrared hyperspectral imaging. Food Chem 2023; 417:135924. [PMID: 36934710 DOI: 10.1016/j.foodchem.2023.135924] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/17/2023]
Abstract
Deoxynivalenol (DON) is the most occurring mycotoxin in oat and oat-based products. Near-infrared hyperspectral imaging (NIR-HSI) has been proposed as a promising methodology for analysing DON contamination in the food industry. The present study aims to apply NIR-HSI for DON detection in oat kernels and to quantify and classify naturally DON-contaminated oat samples. Unground and ground oat samples were scanned by NIR-HSI before their DON content was determined by HPLC. The data were pre-treated and analysed by PLS regression and four classification methods. The most efficient DON prediction model was for unground samples (R2 = 0.75 and RMSEP = 403.18 μg/kg), using twelve characteristic wavelengths with a special interest in 1203 and 1388 nm. The random forest algorithm of unground samples according to the EU maximum limit for unprocessed oats (1750 μg/kg) achieved a classification accuracy of 77.8 %. These findings indicate that NIR-HSI is a promising tool for detecting DON in oats.
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Affiliation(s)
- Irene Teixido-Orries
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Francisco Molino
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Antoni Femenias
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
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15
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Novel Detection Techniques for Shrimp Powder Adulteration Using Near Infrared Spectroscopy in Tandem Chemometric Tools and Multiple Spectral Preprocessing. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02460-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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16
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Hacisalihoglu G, Armstrong P. Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications. PLANTS (BASEL, SWITZERLAND) 2023; 12:1177. [PMID: 36904037 PMCID: PMC10005477 DOI: 10.3390/plants12051177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Seeds play a critical role in ensuring food security for the earth's 8 billion people. There is great biodiversity in plant seed content traits worldwide. Consequently, the development of robust, rapid, and high-throughput methods is required for seed quality evaluation and acceleration of crop improvement. There has been considerable progress in the past 20 years in various non-destructive methods to uncover and understand plant seed phenomics. This review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT). The potential applications of NIR spectroscopy are expected to continue to rise as more seed researchers, breeders, and growers successfully adopt it as a powerful non-destructive method for seed quality phenomics. It will also discuss the advantages and limitations that need to be solved for each technique and how each method could help breeders and industry with trait identification, measurement, classification, and screening or sorting of seed nutritive traits. Finally, this review will focus on the future outlook for promoting and accelerating crop improvement and sustainability.
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Affiliation(s)
- Gokhan Hacisalihoglu
- Biological Sciences Department, Florida A&M University, Tallahassee, FL 32307, USA
| | - Paul Armstrong
- USDA-ARS Center for Grain and Animal Health Research, Manhattan, KS 66502, USA
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17
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Bala M, Sethi S, Sharma S, Mridula D, Kaur G. Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:1294-1302. [PMID: 36098480 DOI: 10.1002/jsfa.12223] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/08/2022] [Accepted: 09/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. RESULTS The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. CONCLUSION The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Manju Bala
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Swati Sethi
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Sanjula Sharma
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - D Mridula
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Gurpreet Kaur
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
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18
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Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Freitag S, Sulyok M, Logan N, Elliott CT, Krska R. The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops. Compr Rev Food Sci Food Saf 2022; 21:5199-5224. [PMID: 36215130 DOI: 10.1111/1541-4337.13054] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/18/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.
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Affiliation(s)
- Stephan Freitag
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Michael Sulyok
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Natasha Logan
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Rudolf Krska
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria.,Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
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20
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Determination of curcumin content in sunflower oil by fourier transform near infrared spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01569-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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21
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Ammeter A, So K, Duncan RW. Analysis of cruciferin content in whole seeds of
Brassica napus
L
. by
near‐infrared
spectroscopy. J AM OIL CHEM SOC 2022. [DOI: 10.1002/aocs.12616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ashley Ammeter
- Department of Plant Science University of Manitoba Winnipeg Canada
| | - Kenny So
- Department of Plant Science University of Manitoba Winnipeg Canada
| | - Robert W. Duncan
- Department of Plant Science University of Manitoba Winnipeg Canada
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22
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Hang J, Shi D, Neufeld J, Bett KE, House JD. Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113669] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2022; 59:3130-3138. [PMID: 35505664 PMCID: PMC9051818 DOI: 10.1007/s13197-022-05456-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 02/08/2022] [Accepted: 03/25/2022] [Indexed: 11/17/2022]
Abstract
The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.
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24
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Chadalavada K, Anbazhagan K, Ndour A, Choudhary S, Palmer W, Flynn JR, Mallayee S, Pothu S, Prasad KVSV, Varijakshapanikar P, Jones CS, Kholová J. NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals. SENSORS 2022; 22:s22103710. [PMID: 35632119 PMCID: PMC9146900 DOI: 10.3390/s22103710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 01/20/2023]
Abstract
Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.
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Affiliation(s)
- Keerthi Chadalavada
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
- Department of Botany, Bharathidasan University, Tiruchirappalli 620 024, India
| | - Krithika Anbazhagan
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | - Adama Ndour
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Bamako BP 320, Mali;
| | - Sunita Choudhary
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | | | | | - Srikanth Mallayee
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | - Sharada Pothu
- South Asia Regional Center, International Livestock Research Institute, Patancheru 502 324, India; (S.P.); (K.V.S.V.P.); (P.V.)
| | | | - Padmakumar Varijakshapanikar
- South Asia Regional Center, International Livestock Research Institute, Patancheru 502 324, India; (S.P.); (K.V.S.V.P.); (P.V.)
| | - Chris S. Jones
- Feed and Forage Development, International Livestock Research Institute, Addis Ababa P.O. Box 5689, Ethiopia;
| | - Jana Kholová
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
- Correspondence:
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25
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Du Z, Tian W, Tilley M, Wang D, Zhang G, Li Y. Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review. Compr Rev Food Sci Food Saf 2022; 21:2956-3009. [PMID: 35478437 DOI: 10.1111/1541-4337.12958] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 01/15/2023]
Abstract
Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.
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Affiliation(s)
- Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
| | - Wenfei Tian
- National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Michael Tilley
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, Kansas, USA
| | - Donghai Wang
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Guorong Zhang
- Agricultural Research Center-Hays, Kansas State University, Hays, Kansas, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
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26
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Li XY, Feng Y, Duan JL, Feng LJ, Wang Q, Ma JY, Liu WZ, Yuan XZ. Model-based mid-infrared spectroscopy for on-line monitoring of volatile fatty acids in the anaerobic digester. ENVIRONMENTAL RESEARCH 2022; 206:112607. [PMID: 34958782 DOI: 10.1016/j.envres.2021.112607] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/12/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
The performance of anaerobic digestion is significantly governed by the concentration of volatile fatty acids (VFAs). Though the titration and near-infrared spectroscopy have been used to measure the VFAs in the digester, there is still lack of the establishment of on-line monitoring of VFAs in practical application. An effective quantification method based on mid-infrared (MIR) spectroscopy was developed, and used to measure the concentrations of VFAs in the anaerobic bioreactor nondestructively in parallel. The wavelet denoising (WD) spectra were used as the spectral preprocessing option. Compared with other pretreatment methods, the established calibration model built by WD spectra showed satisfactory results. Further, the model was verified using high performance liquid chromatography (HPLC), and predictions were made using real reactor effluent samples. Based on this theoretical work, a set of equipment for the in-situ online monitoring of VFAs was designed, which has high feasibility and effectively solves the problems with the current VFAs online monitoring process. These results provide a new solution for on-line monitoring of the anaerobic digestion, and have great potential for practical application.
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Affiliation(s)
- Xiang-Yu Li
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China
| | - Yue Feng
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China; College of Mining and Safety Engineering, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China
| | - Jian-Lu Duan
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China
| | - Li-Juan Feng
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China
| | - Qian Wang
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China
| | - Jing-Ya Ma
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China
| | - Wen-Zong Liu
- Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
| | - Xian-Zheng Yuan
- Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong, 266237, PR China.
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27
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Nagel‐Held J, Kaiser L, H. Longin CF, Hitzmann B. Prediction of Wheat Quality Parameters Combining Raman, Fluorescence and Near‐Infrared Spectroscopy (NIRS). Cereal Chem 2022. [DOI: 10.1002/cche.10540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Johannes Nagel‐Held
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
| | - Leonie Kaiser
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
| | - C. Friedrich H. Longin
- State Plant Breeding Institute University of Hohenheim Fruwirthstraße 21 70599 Stuttgart Germany
| | - Bernd Hitzmann
- Department of Process Analytics and Cereal Science University of Hohenheim, Garbenstraße 23 Stuttgart Germany
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28
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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29
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Mhatre PJ, Joshi M. Human body model with blood flow properties for non-invasive blood glucose measurement. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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30
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Huang J, Wang Z, Fan L, Ma S. A review of wheat starch analyses: Methods, techniques, structure and function. Int J Biol Macromol 2022; 203:130-142. [PMID: 35093434 DOI: 10.1016/j.ijbiomac.2022.01.149] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/28/2021] [Accepted: 01/23/2022] [Indexed: 01/31/2023]
Abstract
Wheat starch has received much attention as an important source of dietary energy for humans, an interesting carbohydrate and a polymeric material. The understanding of the structure and function of wheat starch has always been accompanied by newer technological tools. On the one hand, the general knowledge of wheat starch is constantly being enriched. On the other hand, an increasing number of studies are trying to add new insights to what is already known from two frontier perspectives, namely, wheat starch supramolecular structures and wheat starch fine structures (CLDs). This review describes the structure and function of wheat starch from the perspective of wheat starch analysis techniques (instruments).
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Affiliation(s)
- Jihong Huang
- College of Food and Medicine, Xuchang University, Xuchang, Henan 461000, China; College of Food Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China.
| | - Zhen Wang
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China
| | - Ling Fan
- College of Food and Medicine, Xuchang University, Xuchang, Henan 461000, China
| | - Sen Ma
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China.
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31
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Ahmad M, Vitale R, Silva CS, Ruckebusch C, Cocchi M. A novel proposal to investigate the interplay between the spatial and spectral domains in near-infrared spectral imaging data by means of Image Decomposition, Encoding and Localization (IDEL). Anal Chim Acta 2022; 1191:339285. [PMID: 35033272 DOI: 10.1016/j.aca.2021.339285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 11/06/2021] [Accepted: 11/14/2021] [Indexed: 11/28/2022]
Abstract
The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.
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Affiliation(s)
- Mohamad Ahmad
- Università di Modena e Reggio Emilia, Dipartimento di Scienze Chimiche e Geologiche, Via Campi 103, 41125, Modena, Italy; Univ. Lille, CNRS, LASIRE, LAboratoire de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Cité scientifique, F-59000, Lille, France
| | - Raffaele Vitale
- Univ. Lille, CNRS, LASIRE, LAboratoire de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Cité scientifique, F-59000, Lille, France
| | - Carolina S Silva
- Department of Food Sciences and Nutrition, University of Malta, Msida, 2080, Malta
| | - Cyril Ruckebusch
- Univ. Lille, CNRS, LASIRE, LAboratoire de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Cité scientifique, F-59000, Lille, France
| | - Marina Cocchi
- Università di Modena e Reggio Emilia, Dipartimento di Scienze Chimiche e Geologiche, Via Campi 103, 41125, Modena, Italy.
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32
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Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview. Processes (Basel) 2022. [DOI: 10.3390/pr10020214] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.
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33
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Cozzolino D. An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants. Foods 2022; 11:foods11030315. [PMID: 35159466 PMCID: PMC8834424 DOI: 10.3390/foods11030315] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/06/2022] [Accepted: 01/20/2022] [Indexed: 01/26/2023] Open
Abstract
Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and hardware concomitantly with data analytics, have allowed for the progress in novel applications of these technologies in the field of nutraceutical and bio compound analysis. In recent years, several studies have demonstrated the capability of vibrational spectroscopy to evaluate and/or measure these nutraceuticals in a broad selection of fruit and plants as alternative to classical analytical approaches. This article highlights, as well as discusses, the challenges and opportunities that define the successful application of vibrational spectroscopy techniques, and the advantages that these techniques have to offer to evaluate and quantify nutraceuticals in fruits and plants.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
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34
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Investigations of the Chemical Distribution in Sorbitol and Citric Acid (SorCA) Treated Wood—Development of a Quality Control Method on the Basis of Electromagnetic Radiation. FORESTS 2022. [DOI: 10.3390/f13020151] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent studies showed treatments with sorbitol and citric acid (SorCA) to significantly improve the dimensional stability and biological durability of wood. The industrialization of this process requires a quality control (QC) method to determine if the fixated chemicals are homogenously distributed within the piece of wood, which is essential for uniform material performance. Therefore, the objective of this work was to evaluate the use of common electromagnetic radiation-based methods to determine the degree of modification in SorCA-treated wood. Both Fourier transform infrared (FTIR) spectroscopy and near-infrared (NIR) spectroscopy have been used to create rough calibrations for the weight percent gain (WPG) prediction models. The FTIR measurements resulted in a high linear correlation between the band area ratio (BAR) and the WPG (R2 = 0.93). Additionally, a partial least square (PLS) regression of NIR spectroscopic data resulted in a model with a high prediction power (R2 = 0.83). Furthermore, X-ray density profiling emerged as a simple alternative for the QC by showing a gradient of modification chemicals inside the sample and differences in chemical uptake between earlywood and latewood. Overall, it can be concluded that the results from FTIR, NIR and X-ray densitometry can serve as indicators of impregnation chemical distribution in SorCA-modified wood.
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35
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Advantages, Opportunities, and Challenges of Vibrational Spectroscopy as Tool to Monitor Sustainable Food Systems. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-021-02207-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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36
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Reducing Tillage Affects Long-Term Yields but Not Grain Quality of Maize, Soybeans, Oats, and Wheat Produced in Three Contrasting Farming Systems. SUSTAINABILITY 2022. [DOI: 10.3390/su14020631] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Reducing tillage has been widely promoted to reduce soil erosion, maintain soil health, and sustain long-term food production. The effects of reducing tillage on crop nutritional quality in organic and conventional systems, however, has not been widely explored. One possible driver of crop nutritional quality might be the changing soil nitrogen (N) availability associated with reduced tillage in various management systems. To test how reducing tillage affects crop nutritional quality under contrasting conventional and organic farming systems with varied N inputs, we measured nutritional quality (protein, fat, starch, ash, net energy, total digestible nutrients, and concentrations of Ca, K, Mg, P, and S) of maize, wheat, oats, and soybeans harvested from a long-term trial comprised of three farming systems under two tillage regimes: a conventional grain system (CNV); a low-input organic grain system (LEG); and an organic, manure-based grain + forage system (MNR) under conventional full-tillage (FT) and reduced-till (RT) management. Although maize and wheat yields were 10–13% lower under RT management, grain quality metrics including protein, fat, starch, energy, and mineral concentrations were not significantly affected by reducing tillage. Differences in nutrient quality were more marked between farming systems: protein levels in maize were highest in the MNR system (8.1%); protein levels in soybeans were highest in the LEG system (40.4%); levels of protein (12.9%), ash (2.0%), and sulfur (1430 ppm) in wheat were highest in the CNV system, and oat quality was largely consistent between the LEG and MNR systems. As grain quality did not significantly respond to reducing tillage, other management decisions that affect nutrient availability appear to have a greater effect on nutrient quality.
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37
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A Data Fusion Model to Merge the Spectra Data of Intact and Powdered Cayenne Pepper for the Fast Inspection of Antioxidant Properties. SUSTAINABILITY 2021. [DOI: 10.3390/su14010201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Spectroscopy technology has been widely used for the quality assessment of agricultural products, but the models produced in recent studies usually focus on only one sample form. Meanwhile, most products, especially cayenne pepper, are not only in the form of fresh samples but also in powder. Therefore, the research used visible/near-infrared (Vis/NIR) spectroscopy to predict the antioxidant properties using a fusion model derived from both intact and powdered cayenne pepper. The parameters used to determine these properties include the %inhibition, antioxidant activity, and antioxidant capacity. The results showed that the fusion model at %inhibition was 0.90 (Rcal), 7.63 (RMSEC), 0.84 (Rpred), and 9.16 (RMSEP) while the antioxidant activity had 0.94, 181.82, 0.81 and 340.06, whereas antioxidant capacity produced 0.94, 14.42, 0.82 and 22.64 for Rcal, RMSEC, Rpred, and RMSEP, respectively. The Vis/NIR spectroscopy was able to predict the antioxidant properties in both the intact and powdered cayenne pepper using the fusion model.
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38
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Application of Optical Quality Control Technologies in the Dairy Industry: An Overview. PHOTONICS 2021. [DOI: 10.3390/photonics8120551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.
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39
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The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities. Molecules 2021; 26:molecules26226981. [PMID: 34834073 PMCID: PMC8623772 DOI: 10.3390/molecules26226981] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals.
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40
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Kabir MH, Guindo ML, Chen R, Liu F. Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques. Foods 2021; 10:foods10112767. [PMID: 34829048 PMCID: PMC8623769 DOI: 10.3390/foods10112767] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 01/12/2023] Open
Abstract
Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.
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Affiliation(s)
- Muhammad Hilal Kabir
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
- Department of Agricultural and Bioresource Engineering, Abubakar Tafawa Balewa University, Bauchi PMB 0248, Nigeria
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-571-88982825
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41
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Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02166-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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42
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Siekmann D, Jansen G, Zaar A, Kilian A, Fromme FJ, Hackauf B. A Genome-Wide Association Study Pinpoints Quantitative Trait Genes for Plant Height, Heading Date, Grain Quality, and Yield in Rye ( Secale cereale L.). FRONTIERS IN PLANT SCIENCE 2021; 12:718081. [PMID: 34777409 PMCID: PMC8586073 DOI: 10.3389/fpls.2021.718081] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/22/2021] [Indexed: 06/03/2023]
Abstract
Rye is the only cross-pollinating Triticeae crop species. Knowledge of rye genes controlling complex-inherited traits is scarce, which, currently, largely disables the genomics assisted introgression of untapped genetic variation from self-incompatible germplasm collections in elite inbred lines for hybrid breeding. We report on the first genome-wide association study (GWAS) in rye based on the phenotypic evaluation of 526 experimental hybrids for plant height, heading date, grain quality, and yield in 2 years and up to 19 environments. We established a cross-validated NIRS calibration model as a fast, effective, and robust analytical method to determine grain quality parameters. We observed phenotypic plasticity in plant height and tiller number as a resource use strategy of rye under drought and identified increased grain arabinoxylan content as a striking phenotype in osmotically stressed rye. We used DArTseq™ as a genotyping-by-sequencing technology to reduce the complexity of the rye genome. We established a novel high-density genetic linkage map that describes the position of almost 19k markers and that allowed us to estimate a low genome-wide LD based on the assessed genetic diversity in elite germplasm. We analyzed the relationship between plant height, heading date, agronomic, as well as grain quality traits, and genotype based on 20k novel single-nucleotide polymorphism markers. In addition, we integrated the DArTseq™ markers in the recently established 'Lo7' reference genome assembly. We identified cross-validated SNPs in 'Lo7' protein-coding genes associated with all traits studied. These include associations of the WUSCHEL-related homeobox transcription factor DWT1 and grain yield, the DELLA protein gene SLR1 and heading date, the Ethylene overproducer 1-like protein gene ETOL1 and thousand-grain weight, protein and starch content, as well as the Lectin receptor kinase SIT2 and plant height. A Leucine-rich repeat receptor protein kinase and a Xyloglucan alpha-1,6-xylosyltransferase count among the cross-validated genes associated with water-extractable arabinoxylan content. This study demonstrates the power of GWAS, hybrid breeding, and the reference genome sequence in rye genetics research to dissect and identify the function of genes shaping genetic diversity in agronomic and grain quality traits of rye. The described links between genetic causes and phenotypic variation will accelerate genomics-enabled rye improvement.
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Affiliation(s)
- Dörthe Siekmann
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Agricultural Crops, Sanitz, Germany
- HYBRO Saatzucht GmbH & Co. KG, Schenkenberg, Germany
| | - Gisela Jansen
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | - Anne Zaar
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | | | | | - Bernd Hackauf
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Agricultural Crops, Sanitz, Germany
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43
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Investigation of weight loss in mozzarella cheese using NIR predicted chemical composition and multivariate analysis. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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44
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Stanschewski CS, Rey E, Fiene G, Craine EB, Wellman G, Melino VJ, S. R. Patiranage D, Johansen K, Schmöckel SM, Bertero D, Oakey H, Colque-Little C, Afzal I, Raubach S, Miller N, Streich J, Amby DB, Emrani N, Warmington M, Mousa MAA, Wu D, Jacobson D, Andreasen C, Jung C, Murphy K, Bazile D, Tester M. Quinoa Phenotyping Methodologies: An International Consensus. PLANTS (BASEL, SWITZERLAND) 2021; 10:1759. [PMID: 34579292 PMCID: PMC8472428 DOI: 10.3390/plants10091759] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 11/30/2022]
Abstract
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
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Affiliation(s)
- Clara S. Stanschewski
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Elodie Rey
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Gabriele Fiene
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Evan B. Craine
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Gordon Wellman
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Vanessa J. Melino
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Dilan S. R. Patiranage
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia;
| | - Sandra M. Schmöckel
- Department Physiology of Yield Stability, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Daniel Bertero
- Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires C1417DSE, Argentina;
| | - Helena Oakey
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Carla Colque-Little
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Irfan Afzal
- Department of Agronomy, University of Agriculture, Faisalabad 38000, Pakistan;
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee AB15 8QH, UK;
| | - Nathan Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Dr, Madison, WI 53706, USA;
| | - Jared Streich
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Daniel Buchvaldt Amby
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Nazgol Emrani
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Mark Warmington
- Department of Primary Industries and Regional Development, Agriculture and Food, Kununurra, WA 6743, Australia;
| | - Magdi A. A. Mousa
- Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Vegetables, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
| | - David Wu
- Shanxi Jiaqi Agri-Tech Co., Ltd., Taiyuan 030006, China;
| | - Daniel Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Christian Andreasen
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Christian Jung
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kevin Murphy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Didier Bazile
- CIRAD, UMR SENS, 34398 Montpellier, France;
- SENS, CIRAD, IRD, University Paul Valery Montpellier 3, 34090 Montpellier, France
| | - Mark Tester
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
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Cozzolino D. From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy. ADVANCES IN FOOD AND NUTRITION RESEARCH 2021; 97:119-146. [PMID: 34311898 DOI: 10.1016/bs.afnr.2021.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia.
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46
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Gonçalves DB, Santos CSP, Pinho T, Queirós R, Vaz PD, Bloore M, Satta P, Kovács Z, Casal S, Hoffmann I. Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid, and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition, and Nutritional Value of Five Common Whitefish Species. J AOAC Int 2021; 104:53-60. [PMID: 33619555 DOI: 10.1093/jaoacint/qsaa114] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/14/2020] [Accepted: 08/16/2020] [Indexed: 11/12/2022]
Abstract
Fish fraud is a problematic issue for the industry. For it to be properly addressed will require the use of accurate, rapid, and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids, and moisture) as well as to discriminate between sources (farmed vs. wild fish) and conditions (fresh or defrosted fish). Samples of five whitefish species-Alaskan pollock (Gadus chalcogrammu), Atlantic cod (G. morhua), European plaice (Pleuronectes platessa), common sole (Solea solea), and turbot (Psetta maxima)-including farmed, wild, fresh, and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficients of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA revealed bands at 1150, 1200, and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2, and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address fish fraud and fish QC.
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Affiliation(s)
- Diogo B Gonçalves
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK.,Laboratório de Instrumentação e Partículas, Av. Professor Gama Pinto 2, 1649-003 Lisboa, Portugal
| | - Carla S P Santos
- LAQV/REQUIMTE, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, 4050-313 Porto, Portuga
| | - Teresa Pinho
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK.,LAQV/REQUIMTE, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, 4050-313 Porto, Portuga
| | | | - Pedro D Vaz
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK.,Champalimaud Foundation, Champalimaud Centre for the Unknown, 1400-038 Lisboa, Portugal
| | - Mark Bloore
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK
| | - Paolo Satta
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK
| | - Zoltán Kovács
- Tellspec LTD, 83 Cambridge St, SW1 4PS London, UK.,Szent István University, Department of Physics and Control, Faculty of Food Science, Somlói út 14-16, Budapest H-1118, Hungary
| | - Susana Casal
- LAQV/REQUIMTE, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, 4050-313 Porto, Portuga
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47
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Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin. Food Hydrocoll 2021. [DOI: 10.1016/j.foodhyd.2021.106627] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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48
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Araujo-Andrade C, Bugnicourt E, Philippet L, Rodriguez-Turienzo L, Nettleton D, Hoffmann L, Schlummer M. Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2021; 39:631-651. [PMID: 33749390 PMCID: PMC8165644 DOI: 10.1177/0734242x21997908] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Indexed: 05/06/2023]
Abstract
In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.
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Affiliation(s)
| | | | | | | | | | - Luis Hoffmann
- Fraunhofer Institute for Process Engineering and Packaging IVV, Freising, Germany
| | - Martin Schlummer
- Fraunhofer Institute for Process Engineering and Packaging IVV, Freising, Germany
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49
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Panzitta M, Calamassi N, Sabatini C, Grassi M, Spagnoli C, Vizzini V, Ricchiuto E, Venturini A, Brogi A, Brassier Font J, Pontello L, Bruno G, Minghetti P, Ricci M. Spectrophotometry and pharmaceutical PAT/RTRT: Practical challenges and regulatory landscape from development to product lifecycle. Int J Pharm 2021; 601:120551. [PMID: 33831483 DOI: 10.1016/j.ijpharm.2021.120551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 10/21/2022]
Abstract
European Pharmacopoeia includes dedicated chapters for Raman, NIR and Chemometrics, as well as there is a lot of Academia research on the matter. Despite that, the word innovation is often associated to such tools and there is a still slow implementation at industry. The paper is the outcome of the Associazione Farmaceutici dell'Industria (AFI) Study Group on Process Innovation and Product Lifecycle; the aim is to describe some case studies referring to practical approaches in pharmaceutical industry, in order to depict challenges and opportunities for the implementation of spectroscopic techniques. Case studies include: feasibility and pre-screening evaluations, chemometric model development approaches, way for the method maintenance during commercial manufacturing, challenges for implementation on existing equipment and on sterile processes. Case studies refer to oral solid products, liquid products and sterile Active Pharmaceutical Ingradient (API) manufacturing. There are already successful and robust spectroscopic applications in pharmaceutical industry and the technology is mature: this is the outcome of a strong applied research performed at pharmaceutical production departments. It is necessary to acknowledge efforts done by industry as Research for strengthening the cooperation with Academia, so that advantage of process innovation might reach the patients in a fastest way.
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Affiliation(s)
- Michele Panzitta
- A. Menarini M.L.&S, Via R. Pilo 4, 50131 Firenze, Italy; AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano
| | - Niccolò Calamassi
- Department of Pharmaceutical Sciences, Università degli Studi di Perugia, via del Liceo 1, 06123 Perugia, Italy; AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano.
| | - Cristina Sabatini
- Janssen Cilag, Via C. Janssen, 04100 Latina LT, Italy; AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano
| | - Marzia Grassi
- Janssen Cilag, Via C. Janssen, 04100 Latina LT, Italy; AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano
| | - Chiara Spagnoli
- AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano; Istituto Biochimico Italiano Giovanni Lorenzini, Via Fossignano, 2, 04011 Aprilia (LT), Italy
| | - Vittoria Vizzini
- AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano; Istituto Biochimico Italiano Giovanni Lorenzini, Via Fossignano, 2, 04011 Aprilia (LT), Italy
| | - Elisa Ricchiuto
- AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano; Istituto Biochimico Italiano Giovanni Lorenzini, Via Fossignano, 2, 04011 Aprilia (LT), Italy
| | - Andrea Venturini
- AFI, Study Group on Product Lifecycle and Process Innovation, viale Ranzoni, 1 20041 Milano; Chiesi Italia, Via Palermo, 26/a, 43122 Parma (PR), Italy
| | - Andrea Brogi
- A. Menarini M.L.&S, Via R. Pilo 4, 50131 Firenze, Italy
| | | | - Lino Pontello
- AFI-Associazione Farmaceutici dell'Industria, Viale Ranzoni, 1 20041 Milano
| | - Giorgio Bruno
- AFI-Associazione Farmaceutici dell'Industria, Viale Ranzoni, 1 20041 Milano; Recipharm AB, via Filippo Serperio, 2, Masate (Mi), Italy
| | - Paola Minghetti
- AFI-Associazione Farmaceutici dell'Industria, Viale Ranzoni, 1 20041 Milano; Department of Pharmaceutical Sciences, Università degli Studi di Milano, Via Colombo, 71. 20133 MILANO (MI), Italy
| | - Maurizio Ricci
- Department of Pharmaceutical Sciences, Università degli Studi di Perugia, via del Liceo 1, 06123 Perugia, Italy
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50
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Gohain B, Kumar P, Malhotra B, Augustine R, Pradhan AK, Bisht NC. A comprehensive Vis-NIRS equation for rapid quantification of seed glucosinolate content and composition across diverse Brassica oilseed chemotypes. Food Chem 2021; 354:129527. [PMID: 33756325 DOI: 10.1016/j.foodchem.2021.129527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 02/03/2021] [Accepted: 03/02/2021] [Indexed: 01/28/2023]
Abstract
The globally cultivated Brassica crops contain high deliverable concentrations of health-promoting glucosinolates. Development of a Visible-Near InfraRed Spectroscopy (Vis-NIRS) calibration to profile different glucosinolate components from 641 diverse Brassica juncea chemotypes was attempted in this study. Principal component analysis of HPLC-determined glucosinolates established the distinctiveness of four B. juncea populations used. Subsequently, modified partial least square regression based population-specific and combined Vis-NIRS models were developed, wherein the combined model exhibited higher coefficient of determination (R2; 0.81-0.97) for eight glucosinolates and higher ratio of prediction determination (RPD; 2.42-5.35) for seven glucosinolates in B. juncea populations. Furthermore, range error ratio (RER > 4) for twelve and RER > 10 for eight glucosinolates make the combined model acceptable for screening and quality control. The model also provided excellent prediction for aliphatic glucosinolates in four oilseed Brassica species. Overall, our work highlights the potential of Vis-NIR spectroscopy in estimating glucosinolate content in the economically important Brassica oilseeds.
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Affiliation(s)
- Bornali Gohain
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Pawan Kumar
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Bhanu Malhotra
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Rehna Augustine
- Centre for Plant Biotechnology & Molecular Biology, Kerala Agricultural University, 680656, India.
| | - Akshay K Pradhan
- Department of Genetics, University of Delhi South Campus, New Delhi 110021, India.
| | - Naveen C Bisht
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
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