1
|
Hoffman LC, Schreuder J, Cozzolino D. Food authenticity and the interactions with human health and climate change. Crit Rev Food Sci Nutr 2024:1-14. [PMID: 39101830 DOI: 10.1080/10408398.2024.2387329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.
Collapse
Affiliation(s)
- Louwrens C Hoffman
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
| | - Jana Schreuder
- Food Science Department, Stellenbosch University, Stellenbosch, South Africa
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
| |
Collapse
|
2
|
Liu Y, Xiang Y, Sun W, Degen A, Xu H, Huang Y, Zhong R, Hao L. Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics. J Food Prot 2024; 87:100295. [PMID: 38729244 DOI: 10.1016/j.jfp.2024.100295] [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: 02/09/2024] [Revised: 04/02/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5-year-old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400-2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.
Collapse
Affiliation(s)
- Yuchao Liu
- Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Xining 810016, China; Qinghai Light Industry Research Institute Co., Ltd., Xining 810016, China
| | - Yang Xiang
- Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Xining 810016, China.
| | - Wu Sun
- Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Xining 810016, China
| | - Allan Degen
- Desert Animal Adaptations and Husbandry, Wyler Department of Dryland Agriculture, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beer Sheva 8410500, Israel
| | - Huan Xu
- Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Xining 810016, China
| | - Yayu Huang
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Castanet Tolosan, France
| | - Rongzhen Zhong
- Jilin Province Feed Processing and Ruminant Precision Breeding Cross Regional Cooperation Technology Innovation Center, Jilin Provincial Laboratory of Grassland Farming, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Lizhuang Hao
- Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Xining 810016, China.
| |
Collapse
|
3
|
Wang Y, Xing L, He HJ, Zhang J, Chew KW, Ou X. NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from 'Farm' to 'Table': Progresses, challenges, trends, and prospects. Food Chem X 2024; 22:101449. [PMID: 38784692 PMCID: PMC11112285 DOI: 10.1016/j.fochx.2024.101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/26/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout the entire supply chain. By leveraging NIR data in different wavelength ranges, the physicochemical, nutritional and antioxidant compositions, as well as variety classification of sweetpotato roots during the different stages were adequately evaluated, and all findings involving quantitative and qualitative investigations from the beginning to the present were summarized and analyzed comprehensively. All chemometric algorithms including both linear and nonlinear employed in NIR analysis of sweetpotato roots were introduced in detail and their calibration performances in terms of regression and classification were assessed and discussed. The challenges and limitations of current NIR application in quality evaluation of sweetpotato roots are emphasized. The prospects and trends covering the ongoing advancements in software and hardware are suggested to support the sustainable and efficient sweetpotato processing and utilization.
Collapse
Affiliation(s)
- Yuling Wang
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Longzhu Xing
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jie Zhang
- Henan Xinlianxin Chemical Industry Co., Ltd., Xinxiang 453003, China
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Xingqi Ou
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
| |
Collapse
|
4
|
Vega-Castellote M, Sánchez MT, Torres-Rodríguez I, Entrenas JA, Pérez-Marín D. NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review. Foods 2024; 13:1612. [PMID: 38890841 PMCID: PMC11172355 DOI: 10.3390/foods13111612] [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: 05/02/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.
Collapse
Affiliation(s)
- Miguel Vega-Castellote
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - María-Teresa Sánchez
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - Irina Torres-Rodríguez
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - José-Antonio Entrenas
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - Dolores Pérez-Marín
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| |
Collapse
|
5
|
Li Y, Chen Z, Zhang F, Wei Z, Huang Y, Chen C, Zheng Y, Wei Q, Sun H, Chen F. Research on detection of potato varieties based on spectral imaging analytical algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:123966. [PMID: 38335591 DOI: 10.1016/j.saa.2024.123966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024]
Abstract
Potatoes are popular among consumers due to their high yield and delicious taste. However, due to the numerous varieties of potatoes, different varieties are suitable for different processing methods. Therefore, it is necessary to distinguish varieties after harvest to meet the needs of processing enterprises and consumers. In this study, a new visible-near-infrared spectroscopic analysis method was proposed, which can achieve detection of five potato varieties. The method measures the transmission and reflection spectra of potatoes using a spectral acquisition system, encodes one-dimensional spectra into two-dimensional images using Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF) and Recurrence Plot (RP), and improves the coordinated attention mechanism module and embeds the improved module into the ConvNeXt V2 model to build the ConvNeXt V2-CAP model for potato variety classification. The results show that compared with directly using one-dimensional classification models, image encoding of spectral data for classification greatly improves the accuracy. Among them, the best accuracy of 99.54% is achieved by using GADF image encoding of transmission spectra combined with the ConvNeXt V2-CAP model for classification, which is 16.28% higher than the highest accuracy of the one-dimensional classification model. The CAP attention mechanism module improves the performance of the model, especially when the dataset is small. When the training set is reduced to 150 images, the accuracy of the model is improved by 2.33% compared to the original model. Therefore, it is feasible to classify potato varieties using visible-near infrared spectroscopy and image encoding technology.
Collapse
Affiliation(s)
- You Li
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China
| | - Zhaoqing Chen
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China
| | - Fenyun Zhang
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China
| | - Zhenbo Wei
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang Province 310058, China
| | - Yun Huang
- Jinhua Academy of Agricultural Sciences, Jinhua, Zhejiang Province 321017, China
| | - Changqing Chen
- Jinhua Academy of Agricultural Sciences, Jinhua, Zhejiang Province 321017, China
| | - Yurui Zheng
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China
| | - Qiquan Wei
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China
| | - Hongwei Sun
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China.
| | - Fengnong Chen
- School of Automation, Hangzhou Dianzi University, Hanzhou, Zhejiang Province 310018, China.
| |
Collapse
|
6
|
Li Y, Peng Y, Li Y, Yin T, Wang B. Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion. Foods 2024; 13:1037. [PMID: 38611343 PMCID: PMC11012062 DOI: 10.3390/foods13071037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.
Collapse
Affiliation(s)
| | - Yankun Peng
- College of Engineering, National R&D Center for Agro-Processing Equipment, China Agricultural University, 17 Qinghua East Road, Beijing 100083, China
| | | | | | | |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
John R, Bartwal A, Jeyaseelan C, Sharma P, Ananthan R, Singh AK, Singh M, Gayacharan, Rana JC, Bhardwaj R. Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm. Front Nutr 2023; 10:1224955. [PMID: 38162522 PMCID: PMC10757333 DOI: 10.3389/fnut.2023.1224955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 11/08/2023] [Indexed: 01/03/2024] Open
Abstract
In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.
Collapse
Affiliation(s)
- Racheal John
- Amity Institute of Applied Science, Amity University, Noida, India
| | - Arti Bartwal
- National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research, Pusa, New Delhi, India
| | | | - Paras Sharma
- National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, India
| | - R Ananthan
- National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, India
| | - Amit Kumar Singh
- National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research, Pusa, New Delhi, India
| | - Mohar Singh
- National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research, Pusa, New Delhi, India
| | - Gayacharan
- National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research, Pusa, New Delhi, India
| | - Jai Chand Rana
- The Alliance of Bioversity International & CIAT – India Office, New Delhi, India
| | - Rakesh Bhardwaj
- Germplasm Evaluation Division, National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research (ICAR), New Delhi, India
| |
Collapse
|
9
|
Wang Z, Zuo C, Chen M, Song J, Tu K, Lan W, Li C, Pan L. A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics. Foods 2023; 12:4435. [PMID: 38137239 PMCID: PMC10743185 DOI: 10.3390/foods12244435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/04/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.
Collapse
Affiliation(s)
- Zhenjie Wang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Changzhou Zuo
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Min Chen
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Jin Song
- College of Artificial Intelligence, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Nanjing 210095, China;
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Weijie Lan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Chunyang Li
- Institute of Agro-Products Processing, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Road, Nanjing 210014, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| |
Collapse
|
10
|
Park JW, Jeon J, Kim GB, Jeong KH. Fully Integrated Ultrathin Solid Immersion Grating Microspectrometer for Handheld Visible and Near-Infrared Spectroscopic Applications. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304320. [PMID: 37849223 DOI: 10.1002/advs.202304320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/06/2023] [Indexed: 10/19/2023]
Abstract
Despite advances in microfabrication, compact spectrometers still face challenges in shrinking their size without sacrificing optical performance. Here, the solid immersion grating microspectrometer (SIG-µSPEC) for high spectral resolution in a broad operational wavelength range is reported. The spectroscopic module incorporates a silicon microslit, index-matched lens, plane mirrors, solid immersion grating (SIG), and a CMOS line sensor within a small form factor. The SIG facilitates high angular dispersion of light on a planar focal plane, resulting in an average spectral resolution of 5.8 nm, with over 76% maximum sensitivity from 400 to 800 nm. SIG-µSPEC measures the spectral reflectance of fruits at different ripening stages, clearly revealing changes in the chlorophyll absorption band. The measured spectrum is further utilized for the precise prediction of the soluble solid content (SSC) levels, achieving a high correlation (R2 = 0.91) and a ratio of prediction-to-deviation of 2.36. This compact microspectrometer holds the potential for precise and non-invasive spectral analysis across point-of-care fields.
Collapse
Affiliation(s)
- Jung-Woo Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jaehun Jeon
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gi Beom Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Ki-Hun Jeong
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| |
Collapse
|
11
|
Zadeike D, Degutyte R. Recent Advances in Acoustic Technology in Food Processing. Foods 2023; 12:3365. [PMID: 37761074 PMCID: PMC10530031 DOI: 10.3390/foods12183365] [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: 07/30/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The development of food industry technologies and increasing the sustainability and effectiveness of processing comprise some of the relevant objectives of EU policy. Furthermore, advances in the development of innovative non-thermal technologies can meet consumers' demand for high-quality, safe, nutritious, and minimally processed foods. Acoustic technology is characterized as environmentally friendly and is considered an alternative method due to its sustainability and economic efficiency. This technology provides advantages such as the intensification of processes, increasing the efficiency of processes and eliminating inefficient ones, improving product quality, maintaining the product's texture, organoleptic properties, and nutritional value, and ensuring the microbiological safety of the product. This review summarizes some important applications of acoustic technology in food processing, from monitoring the safety of raw materials and products, intensifying bioprocesses, increasing the effectiveness of the extraction of valuable food components, modifying food polymers' texture and technological properties, to developing biodegradable biopolymer-based composites and materials for food packaging, along with the advantages and challenges of this technology.
Collapse
Affiliation(s)
- Daiva Zadeike
- Department of Food Science and Technology, Faculty of Chemical Technology, Kaunas University of Technology, 50254 Kaunas, Lithuania;
| | | |
Collapse
|
12
|
Peng C, Wang Z. Diagnosis of motor function injury based on near-infrared spectroscopy brain imaging (fNIRS) technology. Prev Med 2023; 174:107641. [PMID: 37481167 DOI: 10.1016/j.ypmed.2023.107641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 07/24/2023]
Abstract
Most clinical stroke patients may have difficulty moving, affecting their self-care ability and quality of life, and causing serious interference with the normal life and work of other family members. At present, in clinical literature, researchers provide functional training for patients with motor disorders through repeated and effective training, which can ultimately effectively promote the recovery of limb function. Therefore, the near-infrared spectroscopy imaging technology (fNIRS) used in this study combines the diagnosis of sports injury with the mechanism of brain function. FNIRS technology has many advantages, such as fast, and non-invasive, and has shown great value in detecting brain activity. Therefore, it has become a promising method in the biomedical field, especially in the field of brain science. Based on the clinical effects of sports injury treatment, fNIRS technology is used to detect the hemodynamic changes of hemoglobin circulation in the patient's brain tissue during training, and to detect the brain activity mechanism in the exercise mechanism, providing a basis for the clinical application of this method.
Collapse
Affiliation(s)
- Cheng Peng
- School of Rehabilitation Medicine, Jiangsu Vocational College Of Medicine, Yancheng, Jiangsu 224000, China.
| | - Ziyi Wang
- School of Rehabilitation Medicine, Jiangsu Vocational College Of Medicine, Yancheng, Jiangsu 224000, China
| |
Collapse
|
13
|
Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
Collapse
Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
| |
Collapse
|
14
|
Alagappan S, Dong A, Mikkelsen D, Hoffman LC, Mantilla SMO, James P, Yarger O, Cozzolino D. Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept. SENSORS (BASEL, SWITZERLAND) 2023; 23:6946. [PMID: 37571729 PMCID: PMC10422329 DOI: 10.3390/s23156946] [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: 06/27/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023]
Abstract
The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.
Collapse
Affiliation(s)
- Shanmugam Alagappan
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
- Fight Food Waste Cooperative Research Centre, Wine Innovation Central Building Level 1, Waite Campus, Urrbrae, SA 5064, Australia
| | - Anran Dong
- School of Agriculture and Food Sustainability, Faculty of Science, University of Queensland, Brisbane, QLD 4072, Australia
| | - Deirdre Mikkelsen
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Agriculture and Food Sustainability, Faculty of Science, University of Queensland, Brisbane, QLD 4072, Australia
| | - Louwrens C. Hoffman
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
- Fight Food Waste Cooperative Research Centre, Wine Innovation Central Building Level 1, Waite Campus, Urrbrae, SA 5064, Australia
- Department of Animal Sciences, University of Stellenbosch, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Sandra Milena Olarte Mantilla
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter James
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Olympia Yarger
- Goterra, 14 Arnott Street, Hume, Canberra, ACT 2620, Australia
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| |
Collapse
|
15
|
Liu H, Liu H, Li J, Wang Y. Rapid and Accurate Authentication of Porcini Mushroom Species Using Fourier Transform Near-Infrared Spectra Combined with Machine Learning and Chemometrics. ACS OMEGA 2023; 8:19663-19673. [PMID: 37305306 PMCID: PMC10249093 DOI: 10.1021/acsomega.3c01229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023]
Abstract
Porcini mushrooms have high nutritional value and great potential, but different species are easily confused, so it is essential to identify them rapidly and precisely. The diversity of nutrients in stipe and cap will lead to differences in spectral information. In this research, Fourier transform near-infrared (FT-NIR) spectral information about imparity species of porcini mushroom stipe and cap was collected and combined into four data matrices. FT-NIR spectra of four data sets were combined with chemometric methods and machine learning for accurate evaluation and identification of different porcini mushroom species. From the results: (1) improved visualization level of t-distributed stochastic neighbor embedding (t-SNE) results after the second derivative preprocessing compared with raw spectra; (2) after using multiple pretreatment combinations to process the four data matrices, the model accuracies based on support vector machine and partial least-square discriminant analysis (PLS-DA) under the best preprocessing method were 98.73-99.04% and 98.73-99.68%, respectively; (3) by comparing the modeling results of FT-NIR spectra with different data matrices, it was found that the PLS-DA model based on low-level data fusion has the highest accuracy (99.68%), but residual neural network (ResNet) model based on the stipe, cap, and average spectral data matrix worked better (100% accuracy). The above results suggest that distinct models should be selected for dissimilar spectral data matrices of porcini mushrooms. Additionally, FT-NIR spectra have the advantages of being nondevastate and fast; this method is expected to be a promising analytical tool in food safety control.
Collapse
Affiliation(s)
- Hong Liu
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
| | - Honggao Liu
- Yunnan
Key Laboratory of Gastrodia and Fungi Symbiotic Biology, Zhaotong University, Zhaotong 657000, Yunnan, China
| | - Jieqing Li
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
| | - Yuanzhong Wang
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
| |
Collapse
|
16
|
Duckena L, Alksnis R, Erdberga I, Alsina I, Dubova L, Duma M. Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy. Foods 2023; 12:foods12101990. [PMID: 37238808 DOI: 10.3390/foods12101990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.
Collapse
Affiliation(s)
- Lilija Duckena
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Reinis Alksnis
- Department of Mathematics, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ieva Erdberga
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ina Alsina
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Laila Dubova
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Mara Duma
- Department of Chemistry, Faculty of Food Technology, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| |
Collapse
|
17
|
Grabska J, Beć KB, Ueno N, Huck CW. Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review. Foods 2023; 12:foods12101946. [PMID: 37238763 DOI: 10.3390/foods12101946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.
Collapse
Affiliation(s)
- Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Krzysztof B Beć
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Nami Ueno
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| |
Collapse
|
18
|
Cai Z, Huang Z, He M, Li C, Qi H, Peng J, Zhou F, Zhang C. Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches. Food Chem 2023; 422:136169. [PMID: 37119596 DOI: 10.1016/j.foodchem.2023.136169] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 04/04/2023] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.
Collapse
Affiliation(s)
- Zeyi Cai
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Zihong Huang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Mengyu He
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fei Zhou
- College of Standardization, China Jiliang University, Hangzhou 310018, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China.
| |
Collapse
|
19
|
Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
Collapse
Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| |
Collapse
|
20
|
Wang Y, Zhang L, Yu X, Zhou C, Yagoub AEA, Li D. A Catalytic Infrared System as a Hot Water Replacement Strategy: A Future Approach for Blanching Fruits and Vegetables to Save Energy and Water. FOOD REVIEWS INTERNATIONAL 2023. [DOI: 10.1080/87559129.2023.2187060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
- Yuqing Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Long Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Xiaojie Yu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Cunshan Zhou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Abu ElGasim Ahmed Yagoub
- Department of Food Science and Nutrition, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dajing Li
- Institute of Farm Product Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, China
| |
Collapse
|
21
|
Zhang Z, Liu H, Wei Z, Lu M, Pu Y, Pan L, Zhang Z, Zhao J, Hu J. A transfer learning method for spectral model of moldy apples from different origins. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
|
22
|
Pang B, Bowker B, Xue CH, Chang YG, Zhang J, Gao L, Zhuang H. Evaluation of visible spectroscopy and low-field nuclear magnetic resonance techniques for screening the presence of defects in broiler breast fillets. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
23
|
Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
24
|
Li J, Zhang Y, Zhang Q, Duan D, Chen L. Establishment of a multi-position general model for evaluation of watercore and soluble solid content in ‘Fuji’ apples using on-line full-transmittance visible and near infrared spectroscopy. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
25
|
RAHMAWATI L, WIDODO S, KURNIADI DP, DAUD P, TRIYONO A, SRIHARTI, SUSANTI ND, MAYASTI NKI, INDRIATI A, YULIANTI LE, PUTRI DP, KUALA SI, ANGGARA CEW, PRISTIANTO EJ, KURNIAWAN ED, APRIYANTO IF, KURNIAWAN D. Determination of colorant type in yellow tofu using Vis-NIR and SW-NIR spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.112422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
| | | | | | | | - Agus TRIYONO
- National Research, and Innovation Agency, Indonesia
| | - SRIHARTI
- National Research, and Innovation Agency, Indonesia
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Khorramifar A, Sharabiani VR, Karami H, Kisalaei A, Lozano J, Rusinek R, Gancarz M. Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy. Foods 2022; 11:foods11244077. [PMID: 36553819 PMCID: PMC9778509 DOI: 10.3390/foods11244077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.
Collapse
Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Vali Rasooli Sharabiani
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
- Correspondence: (H.K.); or (M.G.)
| | - Asma Kisalaei
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Jesús Lozano
- Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. de Elvas S/n, 06006 Badajoz, Spain
| | - Robert Rusinek
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
| | - Marek Gancarz
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Krakow, Poland
- Correspondence: (H.K.); or (M.G.)
| |
Collapse
|
27
|
Authenticity analysis of oregano: development, validation and fitness for use of several food fingerprinting techniques. Food Res Int 2022; 162:111962. [DOI: 10.1016/j.foodres.2022.111962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/18/2022]
|
28
|
Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
29
|
Performance Optimization of a Developed Near-Infrared Spectrometer Using Calibration Transfer with a Variety of Transfer Samples for Geographical Origin Identification of Coffee Beans. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238208. [PMID: 36500300 PMCID: PMC9736488 DOI: 10.3390/molecules27238208] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies-Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67-79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively.
Collapse
|
30
|
Wu Q, Xu L, Zou Z, Wang J, Zeng Q, Wang Q, Zhen J, Wang Y, Zhao Y, Zhou M. Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models. FRONTIERS IN PLANT SCIENCE 2022; 13:1047479. [PMID: 36438117 PMCID: PMC9685660 DOI: 10.3389/fpls.2022.1047479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Collapse
Affiliation(s)
- Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jian Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qifeng Zeng
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Man Zhou
- College of Food Sciences, Sichuan Agricultural University, Yaan, China
| |
Collapse
|
31
|
Ning H, Wang J, Jiang H, Chen Q. Quantitative detection of zearalenone in wheat grains based on near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121545. [PMID: 35767904 DOI: 10.1016/j.saa.2022.121545] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (RP) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.
Collapse
Affiliation(s)
- Hongwei Ning
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiawei Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| |
Collapse
|
32
|
Detection of cumin powder adulteration with allergenic nutshells using FT-IR and portable NIRS coupled with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
33
|
Pandiselvam R, Prithviraj V, Manikantan MR, Kothakota A, Rusu AV, Trif M, Mousavi Khaneghah A. Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review. Front Nutr 2022; 9:973457. [PMID: 36313102 PMCID: PMC9597448 DOI: 10.3389/fnut.2022.973457] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022] Open
Abstract
The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.
Collapse
Affiliation(s)
- R. Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, Kerala, India,*Correspondence: R. Pandiselvam
| | - V. Prithviraj
- Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Sonipat, Haryana, India
| | - M. R. Manikantan
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, Kerala, India,M. R. Manikantan
| | - Anjineyulu Kothakota
- Agro-Processing and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, Kerala, India
| | - Alexandru Vasile Rusu
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, Romania,Animal Science and Biotechnology Faculty, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, Romania
| | - Monica Trif
- Food Research Department, Centre for Innovative Process Engineering (CENTIV) GmbH, Stuhr, Germany,Monica Trif
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Waclaw Dabrowski Institute of Agriculture and Food Biotechnology-State Research Institute, Warsaw, Poland
| |
Collapse
|
34
|
Liu H, Liu H, Li J, Wang Y. Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation. Crit Rev Anal Chem 2022:1-24. [PMID: 36154534 DOI: 10.1080/10408347.2022.2124839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.
Collapse
Affiliation(s)
- Hong Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Honggao Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Zhaotong University, Zhaotong, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| |
Collapse
|
35
|
Liu S, Wang S, Hu C, Zhan S, Kong D, Wang J. Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 277:121261. [PMID: 35490664 DOI: 10.1016/j.saa.2022.121261] [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: 12/05/2021] [Revised: 04/05/2022] [Accepted: 04/10/2022] [Indexed: 06/14/2023]
Abstract
The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring.
Collapse
Affiliation(s)
- Shiyu Liu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shutao Wang
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Chunhai Hu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shujie Zhan
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Deming Kong
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Junzhu Wang
- Flow Measurement Technology Key Lab of Zhejiang Province, College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| |
Collapse
|
36
|
Li Y, Sun H, de Paula Protásio T, Hein PRG, Du B. The mechanisms and prediction of non-structural carbohydrates accretion and depletion after mechanical wounding in slash pine (Pinus elliottii) using near-infrared reflectance spectroscopy. PLANT METHODS 2022; 18:107. [PMID: 36050789 PMCID: PMC9434866 DOI: 10.1186/s13007-022-00939-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The allocation of non-structural carbohydrates (NSCs) plays a critical role in the physiology and metabolism of tree growth and survival defense. However, little is known about the allocation of NSC after continuous mechanical wounding of pine by resin tapping during tree growth. RESULTS Here, we examine the NSC allocation in plant tissues after 3 year lasting resin tapping, and also investigate the use of near-infrared reflectance (NIR) spectroscopy to quantify the NSC, starch and free sugar (e.g., sucrose, glucose, and fructose) concentrations in different plant tissues of slash pine. Spectral measurements on pine needle, branch, trunk phloem, and root were obtained before starch and free sugar concentrations were measured in the laboratory. The variation of NSC, starch and free sugars in different plant tissues after resin tapping was analyzed. Partial least squares regression was applied to calibrate prediction models, models were simulated 100 times for model performance and error estimation. More NSC, starch and free sugars were stored in winter than summer both in tapped and control trees. The position of resin tapping significantly influenced the NSCs allocation in plant tissues: more NSCs were transformed into free sugars for defensive resin synthesis close to the tapping wound rather than induced distal systemic responses. Models for predicting NSC and free sugars of plant tissues showed promising results for the whole tree for fructose (R2CV = 0.72), glucose (R2CV = 0.67), NSCs (R2CV = 0.66) and starch (R2CV = 0.58) estimates based on NIR models. Models for individual plant tissues also showed reasonable predictive ability: the best model for NSCs and starch prediction was found in root. The significance multivariate correlation algorithm for variable selection significantly reduced the number of variables. Important variables were identified, including features at 1021-1290 nm, 1480, 1748, 1941, 2020, 2123 and 2355 nm, which are highly related to NSC, starch, fructose, glucose and sucrose. CONCLUSIONS NIR spectroscopy provided a rapid and cost-effective method to monitor NSC, starch and free sugar concentrations after continuous resin tapping. It can be used for studying the trade-off between growth and production of defensive metabolites.
Collapse
Affiliation(s)
- Yanjie Li
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, 311400, China
| | - Honggang Sun
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, 311400, China.
| | - Thiago de Paula Protásio
- Federal Rural University of Amazonia-UFRA, Campus Parauapebas, Parauapebas, Pará, 68515-000, Brazil
| | | | - Baoguo Du
- Chair of Tree Physiology, Institute of Forest Sciences, AlbertLudwigs-Universitat Freiburg, Georges-Koehler-Allee 53, 79110, Freiburg, Germany
| |
Collapse
|
37
|
Ghanei Ghooshkhaneh N, Golzarian MR, Mollazade K. VIS-NIR spectroscopy for detection of citrus core rot caused by Alternaria alternata. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
38
|
Liu X, Le Bourvellec C, Yu J, Zhao L, Wang K, Tao Y, Renard CM, Hu Z. Trends and challenges on fruit and vegetable processing: Insights into sustainable, traceable, precise, healthy, intelligent, personalized and local innovative food products. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
39
|
Tirado-Kulieva VA, Hernández-Martínez E, Suomela JP. Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04023-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractThe constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.
Collapse
|
40
|
Non-destructive determination of color, titratable acidity, and dry matter in intact tomatoes using a portable Vis-NIR spectrometer. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
41
|
Yu D, Lord N, Polk J, Dhakal K, Li S, Yin Y, Duncan SE, Wang H, Zhang B, Huang H. Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time. Food Chem 2022; 368:130799. [PMID: 34425343 DOI: 10.1016/j.foodchem.2021.130799] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 08/04/2021] [Accepted: 08/04/2021] [Indexed: 12/31/2022]
Abstract
This study aims to investigate the changes in physical and chemical properties of edamame during bean development and apply a spectroscopy-based machine learning (ML) technique to determine optimal harvest time. The edamame harvested at R5 (beginning seed), R6 (full seed), and R7 (beginning maturity) growth stages were characterized for physical and chemical properties, and pods were measured for spectral reflectance (360-740 nm) using a handheld spectrophotometer. The samples were categorized into 'early', 'ready', and 'late' based on the characterized properties. The results showed that pod/bean weight and pod thickness peaked at R6 and remained stable thereafter. Sugar, starch, alanine, and glycine also peaked at R6 but proceeded to decline. The ML method (random forest classification) using pods' spectral reflectance had a high accuracy of 0.95 for classifying 'early' and 'late' samples and 0.87 for classifying 'early' and 'ready' samples. Therefore, this method can determine the optimal harvest time of edamame.
Collapse
Affiliation(s)
- Dajun Yu
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Nick Lord
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Justin Polk
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Kshitiz Dhakal
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Yun Yin
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Susan E Duncan
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Hengjian Wang
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States.
| | - Haibo Huang
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States.
| |
Collapse
|
42
|
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.
Collapse
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
| |
Collapse
|
43
|
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]
|
44
|
Mohd Ali M, Hashim N. Non-destructive methods for detection of food quality. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00003-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
|
45
|
Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Curr Res Food Sci 2022; 5:1305-1312. [PMID: 36065198 PMCID: PMC9440252 DOI: 10.1016/j.crfs.2022.08.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022] Open
Abstract
The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products. NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature. Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis. Online and handheld NIR spectrometers for wheat flour detection are the future trends.
Collapse
|
46
|
Rapid Analysis of Fruit Acids by Laser-Engraved Free-Standing Terahertz Metamaterials. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02176-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
47
|
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.
Collapse
|
48
|
Loffredi E, Grassi S, Alamprese C. Spectroscopic approaches for non-destructive shell egg quality and freshness evaluation: Opportunities and challenges. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
49
|
Monago-Maraña O, Cabrera-Bañegil M, Rodas NL, Muñoz de la Peña A, Durán-Merás I. First-order discrimination of methanolic extracts from plums according to harvesting date using fluorescence spectra. Quantification of polyphenols. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106533] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
|
50
|
Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy. SUSTAINABILITY 2021. [DOI: 10.3390/su131910747] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.
Collapse
|