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Foli LP, Hespanhol MC, Cruz KAML, Pasquini C. Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124297. [PMID: 38640625 DOI: 10.1016/j.saa.2024.124297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.
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Affiliation(s)
- Letícia P Foli
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Maria C Hespanhol
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Kaíque A M L Cruz
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Celio Pasquini
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Rua Monteiro Lobato, 290, Campinas, SP 13083-862, Brazil.
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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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Yang Y, Harrison RC, Zhang D, Shen B, Yan Y, Kang D. Effect of genetic distances of different genotypes of maize on the authenticity of single seeds detected by NIR spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1361328. [PMID: 38486851 PMCID: PMC10937569 DOI: 10.3389/fpls.2024.1361328] [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: 12/25/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Introduction NIR spectroscopy combined with chemometric algorithms has been widely used for seed authenticity detection. However, the study of seed genetic distance, an internal feature that affects the discriminative performance of classification models, has rarely been reported. Methods Therefore, maize seed samples of different genotypes were selected to investigate the effect of genetic distance on the authenticity of single seeds detected by NIR spectroscopy. Firstly, the Support vector machine (SVM) model was established using spectral information combined with a preprocessing algorithm, and then the DNA of the samples was extracted to study the correlation between genetic and relative spectral distances, the model identification performance, and finally to compare the similarities and differences between the results of genetic clustering and relative spectral clustering. Results The results were as follows: the average accuracy of the models was 93.6% (inbred lines) and 93.7% (hybrids), respectively; Genetic distance and correlation spectral distance exhibited positive correlation significantly (inbred lines: r=0.177, p<0.05; hybrids: r=0.238, p<0.05), likewise genetic distance and model accuracy also showed positive correlation (inbred lines: r=0.611, p<0.01; hybrids: r=0.6158, p<0.01); Genetic clustering and spectral clustering results were essentially uniform for 94.3% (inbred lines) and 93.9% (hybrids), respectively. Discussion This study reveals the relationship between the genetic and relative spectral distances of seeds and the accuracy of the model, which provides theoretical basis for the establishment of the standardized system for detecting the authenticity of seeds by NIR spectroscopic techniques.
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Affiliation(s)
- Yongqin Yang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Rashaun Candace Harrison
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Dun Zhang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Binghui Shen
- Department of Applied Physics, College of Science, China Agricultural University, Beijing, China
| | - Yanlu Yan
- Department of Electrical Engineering, College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Dingming Kang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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Gorla G, Taborelli P, Giussani B. A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data. Molecules 2023; 28:7999. [PMID: 38138488 PMCID: PMC10745448 DOI: 10.3390/molecules28247999] [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: 11/15/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals.
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Affiliation(s)
| | | | - Barbara Giussani
- Department of Science and High Technology, University of Insubria, Via Valleggio 9, 22100 Como, Italy
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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Peiris KHS, Bean SR, Wu X, Sexton-Bowser SA, Tesso T. Performance of a Handheld MicroNIR Instrument for Determining Protein Levels in Sorghum Grain Samples. Foods 2023; 12:3101. [PMID: 37628100 PMCID: PMC10453391 DOI: 10.3390/foods12163101] [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: 07/06/2023] [Revised: 08/08/2023] [Accepted: 08/12/2023] [Indexed: 08/27/2023] Open
Abstract
Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R2) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.
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Affiliation(s)
- Kamaranga H. S. Peiris
- Grain Quality and Structure Research Unit, Center for Grain and Animal Health Research, USDA-ARS, Manhattan, KS 66502, USA; (K.H.S.P.); (X.W.)
| | - Scott R. Bean
- Grain Quality and Structure Research Unit, Center for Grain and Animal Health Research, USDA-ARS, Manhattan, KS 66502, USA; (K.H.S.P.); (X.W.)
| | - Xiaorong Wu
- Grain Quality and Structure Research Unit, Center for Grain and Animal Health Research, USDA-ARS, Manhattan, KS 66502, USA; (K.H.S.P.); (X.W.)
| | - Sarah A. Sexton-Bowser
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA; (S.A.S.-B.); (T.T.)
| | - Tesfaye Tesso
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA; (S.A.S.-B.); (T.T.)
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Kashani Zadeh H, Hardy M, Sueker M, Li Y, Tzouchas A, MacKinnon N, Bearman G, Haughey SA, Akhbardeh A, Baek I, Hwang C, Qin J, Tabb AM, Hellberg RS, Ismail S, Reza H, Vasefi F, Kim M, Tavakolian K, Elliott CT. Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115149. [PMID: 37299875 DOI: 10.3390/s23115149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.
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Affiliation(s)
| | - Mike Hardy
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | - Mitchell Sueker
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Yicong Li
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | | | | | - Simon A Haughey
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | - Insuck Baek
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Chansong Hwang
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Jianwei Qin
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Amanda M Tabb
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Rosalee S Hellberg
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Shereen Ismail
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | - Hassan Reza
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | | | - Moon Kim
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, Khong Luang 12120, Thailand
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Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
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9
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Food forensics: techniques for authenticity determination of food products. Forensic Sci Int 2022; 333:111243. [DOI: 10.1016/j.forsciint.2022.111243] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/21/2022]
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10
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Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview. Processes (Basel) 2022. [DOI: 10.3390/pr10020214] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.
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Nkouaya Mbanjo EG, Hershberger J, Peteti P, Agbona A, Ikpan A, Ogunpaimo K, Kayondo SI, Abioye RS, Nafiu K, Alamu EO, Adesokan M, Maziya-Dixon B, Parkes E, Kulakow P, Gore MA, Egesi C, Rabbi IY. Predicting starch content in cassava fresh roots using near-infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:990250. [PMID: 36426140 PMCID: PMC9679500 DOI: 10.3389/fpls.2022.990250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/14/2022] [Indexed: 05/20/2023]
Abstract
The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350-2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400-2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84-0.90; ratio of performance deviation (RPD): 2.49-3.11, ratio of performance to interquartile distance (RPIQ): 3.24-4.08, concordance correlation coefficient (CCC): 0.91-0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871-1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.
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Affiliation(s)
- Edwige Gaby Nkouaya Mbanjo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- *Correspondence: Edwige Gaby Nkouaya Mbanjo,
| | - Jenna Hershberger
- Department of Plant and Environmental Sciences, Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Prasad Peteti
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX, United States
| | - Andrew Ikpan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kayode Ogunpaimo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Siraj Ismail Kayondo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Racheal Smart Abioye
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kehinde Nafiu
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | | | - Michael Adesokan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Busie Maziya-Dixon
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Elizabeth Parkes
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- National Root Crops Research Institute (NRCRI), Umuahia, Nigeria
| | - Ismail Yusuf Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
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12
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Dashti A, Müller-Maatsch J, Weesepoel Y, Parastar H, Kobarfard F, Daraei B, AliAbadi MHS, Yazdanpanah H. The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification. Foods 2021; 11:71. [PMID: 35010197 PMCID: PMC8750306 DOI: 10.3390/foods11010071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022] Open
Abstract
Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400-1000 nm) and a handheld NIR (900-1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95-100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.
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Affiliation(s)
- Abolfazl Dashti
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
| | - Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Yannick Weesepoel
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran P.O. Box 11155-9516, Iran;
| | - Farzad Kobarfard
- Department of Medicinal Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran;
| | - Bahram Daraei
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
| | | | - Hassan Yazdanpanah
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
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Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.11.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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