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Zhu Y, Fan S, Zuo M, Zhang B, Zhu Q, Kong J. Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning. Foods 2024; 13:1570. [PMID: 38790869 PMCID: PMC11120509 DOI: 10.3390/foods13101570] [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: 04/19/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899-1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky-Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1 derivative (SG-D1), Savitzky-Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.
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Affiliation(s)
- Yanqiu Zhu
- Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment of Jiangsu University, Zhenjiang 212013, China;
| | - Shuxiang Fan
- College of Technology, Beijing Forestry University, Beijing 100083, China;
| | - Min Zuo
- National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China;
| | - Baohua Zhang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China;
| | - Qingzhen Zhu
- Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment of Jiangsu University, Zhenjiang 212013, China;
| | - Jianlei Kong
- National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China;
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Xiao Y, Zhang X, Liu J, Li H, Jiang J, Li Y, Diao S. Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques. FRONTIERS IN PLANT SCIENCE 2024; 15:1346192. [PMID: 38766470 PMCID: PMC11099265 DOI: 10.3389/fpls.2024.1346192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/17/2024] [Indexed: 05/22/2024]
Abstract
Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.
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Affiliation(s)
- Yuguang Xiao
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
| | - Xiaoshu Zhang
- School of Civil Engineering and Architecture, Xinxiang University, Xinxiang, China
| | - Jun Liu
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
| | - He Li
- Research Institute of Landscape Plants, Guizhou Academy of Forestry, Guiyang, China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
| | - Yanjie Li
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
| | - Shu Diao
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
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Alaoui Mansouri M, Kharbach M, Bouklouze A. Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review. J Pharm Sci 2024; 113:856-865. [PMID: 38072117 DOI: 10.1016/j.xphs.2023.12.004] [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: 09/06/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/22/2023]
Abstract
The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.
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Affiliation(s)
- Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, FI-90014 Oulu, Finland; University of Liege (ULiege), CIRM, Vibra-Santé HUB, Laboratory of Pharmaceutical Analytical Chemistry, CHU, B36, B-4000, Liege, Belgium.
| | - Mourad Kharbach
- Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland.
| | - Abdelaziz Bouklouze
- Bio-Pharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat, Morocco
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Huang J, Wang P, Wu Y, Zeng L, Ji X, Zhang X, Wu M, Tong H, Yang Y. Rapid determination of triglyceride and glucose levels in Drosophila melanogaster induced by high-sugar or high-fat diets based on near-infrared spectroscopy. Heliyon 2023; 9:e17389. [PMID: 37426790 PMCID: PMC10329124 DOI: 10.1016/j.heliyon.2023.e17389] [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: 03/17/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Abstract
Triglyceride and glucose levels are important indicators for determining metabolic syndrome, one of the leading public-health burdens worldwide. Drosophila melanogaster is an ideal model for investigating metabolic diseases because it has 70% homology to human genes and its regulatory mechanism of energy metabolism homeostasis is highly similar to that of mammals. However, traditional analytical methods of triglyceride and glucose are time-consuming, laborious, and costly. In this study, a simple, practical, and reliable near-infrared (NIR) spectroscopic analysis method was developed for the rapid determination of glucose and triglyceride levels in an in vivo model of metabolic disorders using Drosophila induced by high-sugar or high-fat diets. The partial least squares (PLS) model was constructed and optimized using different spectral regions and spectral pretreatment methods. The overall results had satisfactory prediction performance. For Drosophila induced by high-sugar diets, the correlation coefficient (RP) and root mean square error of prediction (RMSEP) were 0.919 and 0.228 mmoL gprot-1 for triglyceride and 0.913 and 0.143 mmoL gprot-1 for glucose respectively; for Drosophila induced by high-fat diets, the RP and RMSEP were 0.871 and 0.097 mmoL gprot-1 for triglyceride and 0.853 and 0.154 mmoL gprot-1 for glucose, respectively. This study demonstrated the potential of using NIR spectroscopy combined with PLS in the determination of triglyceride and glucose levels in Drosophila, providing a rapid and effective method for monitoring metabolite levels during disease development and a possibility for evaluating metabolic diseases in humans in clinical practice.
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Affiliation(s)
- Jiamin Huang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Pengwei Wang
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China
| | - Yu Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Li Zeng
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China
| | - Xu Zhang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Mingjiang Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Haibin Tong
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
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da Rosa G, Dazuk V, Galli GM, Alba DF, Boiago MM, Oliveira FC, Siebeneichler TJ, Zambiazi RC, Galli V, Copetti PM, Schetinger MR, Wagner R, Meinhart AD, Da Silva AS. The addition of residue from pruning of yerba mate (Ilex paraguariensis) in laying hens modulates fatty acid profile and incorporates chlorogenic acid in the egg. Res Vet Sci 2022; 147:28-36. [DOI: 10.1016/j.rvsc.2022.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/06/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022]
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6
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Yang Y, She X, Cao X, Yang L, Huang J, Zhang X, Su L, Wu M, Tong H, Ji X. Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 277:121249. [PMID: 35483257 DOI: 10.1016/j.saa.2022.121249] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/19/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (Rp2) in the independent test sets were 12.7768 g kg-1 and 0.9586, 1.1346 g kg-1 and 0.9670, 0.3938 g kg-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.
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Affiliation(s)
- Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Xiangting She
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xiaoqing Cao
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Liuchang Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Jiamin Huang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xu Zhang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Laijin Su
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Mingjiang Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Haibin Tong
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
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7
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Li L, Li ZM, Wang YZ. A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves. PLANT METHODS 2022; 18:102. [PMID: 35964064 PMCID: PMC9375363 DOI: 10.1186/s13007-022-00935-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy. RESULTS (1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions. CONCLUSIONS This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.
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Affiliation(s)
- Lian Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, People's Republic of China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, People's Republic of China
| | - Zhi Min Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, People's Republic of China.
| | - Yuan Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, People's Republic of China.
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Fermentation process monitoring of broad bean paste quality by NIR combined with chemometrics. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01392-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Yang Y, Yang L, He S, Cao X, Huang J, Ji X, Tong H, Zhang X, Wu M. Use of near-infrared spectroscopy and chemometrics for fast discrimination of Sargassum fusiforme. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Wang R, Wei X, Wang H, Zhao L, Zeng C, Wang B, Zhang W, Liu L, Xu Y. Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content in Wheat Grains. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:5599388. [PMID: 34336359 PMCID: PMC8298176 DOI: 10.1155/2021/5599388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/05/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
The chemical method for the determination of the resistant starch (RS) content in grains is time-consuming and labor intensive. Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy are rapid and nondestructive analytical techniques for determining grain quality. This study was the first report to establish and compare these two spectroscopic techniques for determining the RS content in wheat grains. Calibration models with four preprocessing techniques based on the partial least squares (PLS) algorithm were built. In the NIR technique, the mean normalization + Savitzky-Golay smoothing (MN + SGS) preprocessing technique had a higher coefficient of determination (R c 2 = 0.672; R p 2 = 0.552) and a relative lower root mean square error value (RMSEC = 0.385; RMSEP = 0.459). In the ATR-MIR technique, the baseline preprocessing method exhibited a better performance regarding to the values of coefficient of determination (R c 2 = 0.927; R p 2 = 0.828) and mean square error value (RMSEC = 0.153; RMSEP = 0.284). The validation of the developed best NIR and ATR-MIR calibration models showed that the ATR-MIR best calibration model has a better RS prediction ability than the NIR best calibration model. Two high grain RS content wheat mutants were screened out by the ATR-MIR best calibration model from the wheat mutant library. There was no significant difference between the predicted values and chemical measured values in the two high RS content mutants. It proved that the ATR-MIR model can be a perfect substitute in RS measuring. All the results indicated that the ATR-MIR spectroscopy with improved screening efficiency can be used as a fast, rapid, and nondestructive method in high grain RS content wheat breeding.
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Affiliation(s)
- Rong Wang
- Hubei Key Laboratory of Waterlogging Disaster and Agriculture Use of Wetland and Hubei Collaborative Innovation Centre for Grain Industry and Engineering Research Center of Ecology and Agriculture Use of Wetland, Ministry of Education, Yangtze University, Jingzhou, Hubei 434025, China
| | - Xia Wei
- Hubei Key Laboratory of Waterlogging Disaster and Agriculture Use of Wetland and Hubei Collaborative Innovation Centre for Grain Industry and Engineering Research Center of Ecology and Agriculture Use of Wetland, Ministry of Education, Yangtze University, Jingzhou, Hubei 434025, China
- Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Food Crops Institute, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
| | - Hongpan Wang
- Hubei Key Laboratory of Waterlogging Disaster and Agriculture Use of Wetland and Hubei Collaborative Innovation Centre for Grain Industry and Engineering Research Center of Ecology and Agriculture Use of Wetland, Ministry of Education, Yangtze University, Jingzhou, Hubei 434025, China
| | - Linshu Zhao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Cengli Zeng
- Hubei Engineering Research Center for Protection and Utilization of Special Biological Resources in the Hanjiang River Basin, Jianghan University, Wuhan 430056, China
| | - Bingrui Wang
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430064, China
| | - Wenying Zhang
- Hubei Key Laboratory of Waterlogging Disaster and Agriculture Use of Wetland and Hubei Collaborative Innovation Centre for Grain Industry and Engineering Research Center of Ecology and Agriculture Use of Wetland, Ministry of Education, Yangtze University, Jingzhou, Hubei 434025, China
| | - Luxiang Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yanhao Xu
- Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Food Crops Institute, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
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Pires FDC, Pereira RGFA, Baqueta MR, Valderrama P, Alves da Rocha R. Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees. Food Chem 2021; 365:130471. [PMID: 34252622 DOI: 10.1016/j.foodchem.2021.130471] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/14/2021] [Accepted: 06/24/2021] [Indexed: 11/30/2022]
Abstract
Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control.
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Affiliation(s)
| | | | - Michel Rocha Baqueta
- Universidade Tecnológica Federal do Paraná (UTFPR), Campo Mourão, PR 87301-899, Brazil
| | - Patrícia Valderrama
- Universidade Tecnológica Federal do Paraná (UTFPR), Campo Mourão, PR 87301-899, Brazil.
| | - Roney Alves da Rocha
- Engineering Department, Federal University of Lavras (UFLA), Lavras, MG 37200-000, Brazil.
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12
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Jiang H, He Y, Chen Q. Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3328-3335. [PMID: 33222172 DOI: 10.1002/jsfa.10962] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. CONCLUSION The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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13
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Zhu J, Wang Q, Han L, Zhang C, Wang Y, Tu K, Peng J, Wang J, Pan L. Effects of caprolactam content on curdlan-based food packaging film and detection by infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118942. [PMID: 32977105 DOI: 10.1016/j.saa.2020.118942] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 09/01/2020] [Accepted: 09/03/2020] [Indexed: 06/11/2023]
Abstract
In this study, we report a rapid statistical approach used in determining the caprolactam (CPL) content in curdlan packaging films, which is based on the spectral data observed in the near-infrared (NIR) and Mid-infrared (MIR) regions. At the first stage of the study, the CPL content was added into the curdlan films prepared by controlling the concentration, and then the effect of the CPL concentration on the measured mechanical properties of the produced films were evaluated. At the next stage, the NIR and MIR spectra of the curdlan films with different CPL concentrations were recorded by using the FT-NIR and FT-IR spectroscopy technique, and the spectral data to be used in the regression models in our quantitative analyses were carefully selected. It was observed that the curdlan film with 5% CPL exhibited the best mechanical properties. The obtained best correlation parameters which are used in evaluation of CPL content through the observed NIR and MIR spectral data are Rp = 0.9552, RMSEP = 1.2506 (NIR); Rp = 0.9092 and RMSEP = 1.9136 (MIR), respectively. These optimal values support the expectation that our statistical approach based on NIR and MIR data can provide a rapid, accurate and nondestructive way of determining CPL content in curdlan packaging films.
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Affiliation(s)
- Jingyi Zhu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Qian Wang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Lu Han
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Chong Zhang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yuanyuan Wang
- Institute of Zhongqing Food Safety Inspection and Testing, Anhui Zhongqing Inspection and Testing Co. LTD, Hefei, Anhui 230088, China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Jing Peng
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Jiahong Wang
- College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China.
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14
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Franceschelli L, Berardinelli A, Crescentini M, Iaccheri E, Tartagni M, Ragni L. A Non-Invasive Soil Moisture Sensing System Electronic Architecture: A Real Environment Assessment. SENSORS 2020; 20:s20216147. [PMID: 33137922 PMCID: PMC7663388 DOI: 10.3390/s20216147] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 11/16/2022]
Abstract
This paper will show the electronic architecture of a portable and non-invasive soil moisture system based on an open rectangular waveguide. The spectral information, measured in the range of 1.5–2.7 GHz, is elaborated on by an embedded predictive model, based on a partial least squares (PLS) regression tool, for the estimation of the soil moisture (%) in a real environment. The proposed system is composed of a waveguide, containing Tx and Rx antennas, and an electronic circuit driven by a microcontroller (MCU). It will be shown how the system provides a useful and fast estimation of moisture on a silty clay loam soil characterized by a moisture range of about 9% to 32% and a soil temperature ranging from about 8 °C and 18 °C. Using the PLS approach, the moisture content can be predicted with an R2 value of 0.892, a root mean square error (RMSE) of 1.0%, and a residual prediction deviation (RPD) of 4.3. The results prove that it is possible to make accurate and rapid moisture assessments without the use of invasive electrodes, as currently employed by state-of-the-art approaches.
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Affiliation(s)
- Leonardo Franceschelli
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”—University of Bologna, Via dell’Università, 50, 47521 Cesena, Italy; (M.C.); (M.T.)
- Correspondence:
| | - Annachiara Berardinelli
- Department of Industrial Engineering, University of Trento, Via Sommarive, 9, 38123 Povo, Italy;
- Centre Agriculture Food Environment, University of Trento, Via E. Mach, 1, 38010 S. Michele all’Adige, Italy
| | - Marco Crescentini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”—University of Bologna, Via dell’Università, 50, 47521 Cesena, Italy; (M.C.); (M.T.)
| | - Eleonora Iaccheri
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy; (E.I.); (L.R.)
- Interdepartmental Center for Industrial Agri-Food Research, University of Bologna, Via Q. Bucci 336, 47521 Cesena, Italy
| | - Marco Tartagni
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”—University of Bologna, Via dell’Università, 50, 47521 Cesena, Italy; (M.C.); (M.T.)
| | - Luigi Ragni
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy; (E.I.); (L.R.)
- Interdepartmental Center for Industrial Agri-Food Research, University of Bologna, Via Q. Bucci 336, 47521 Cesena, Italy
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15
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Could antioxidant capacity and flavonoid content of ethanolic extracts of geopropolis from Brazilian native bees be estimated from digital photos and NIR Spectra? Microchem J 2020. [DOI: 10.1016/j.microc.2020.105031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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