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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.
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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
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Nakatumba-Nabende J, Babirye C, Tusubira JF, Mutegeki H, Nabiryo AL, Murindanyi S, Katumba A, Nantongo J, Sserunkuma E, Nakitto M, Ssali R, Makunde G, Moyo M, Campos H. Using machine learning for image-based analysis of sweetpotato root sensory attributes. SMART AGRICULTURAL TECHNOLOGY 2023; 5:None. [PMID: 37800125 PMCID: PMC10547598 DOI: 10.1016/j.atech.2023.100291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 10/07/2023]
Abstract
The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as high-throughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained R 2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained R 2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R 2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.
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Affiliation(s)
| | - Claire Babirye
- Makerere Artificial Intelligence Lab, Makerere University, Uganda
| | | | - Henry Mutegeki
- Makerere Artificial Intelligence Lab, Makerere University, Uganda
| | - Ann Lisa Nabiryo
- Makerere Artificial Intelligence Lab, Makerere University, Uganda
| | | | - Andrew Katumba
- Department of Electrical and Computer Engineering, Makerere University, Uganda
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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He HJ, Wang Y, Wang Y, Liu H, Zhang M, Ou X. Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [ Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging. Food Chem X 2023; 18:100631. [PMID: 36926310 PMCID: PMC10010985 DOI: 10.1016/j.fochx.2023.100631] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2023] [Accepted: 03/05/2023] [Indexed: 03/10/2023] Open
Abstract
This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.,School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Yangyang Wang
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
| | - Mian Zhang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
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Wang YQ, Liu GM, Hu LP, Zhao XZ, Zhang DS, He HJ. Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy. Foods 2023; 12:foods12091922. [PMID: 37174459 PMCID: PMC10178596 DOI: 10.3390/foods12091922] [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: 03/22/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
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Affiliation(s)
- Ya-Qin Wang
- Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
| | - Guang-Min Liu
- Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
| | - Li-Ping Hu
- Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
| | - Xue-Zhi Zhao
- Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
| | - De-Shuang Zhang
- Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Hong-Ju He
- Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Jayapal PK, Joshi R, Sathasivam R, Van Nguyen B, Faqeerzada MA, Park SU, Sandanam D, Cho BK. Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions. FRONTIERS IN PLANT SCIENCE 2022; 13:982247. [PMID: 36119609 PMCID: PMC9478847 DOI: 10.3389/fpls.2022.982247] [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: 06/30/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400-1,000 nm (VIS/NIR) and 900-2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants' total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R 2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.
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Affiliation(s)
- Praveen Kumar Jayapal
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART), Singapore, Singapore
| | - Rahul Joshi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Ramaraj Sathasivam
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Bao Van Nguyen
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Sang Un Park
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, South Korea
| | - Domnic Sandanam
- Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, South Korea
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Md Saleh R, Kulig B, Arefi A, Hensel O, Sturm B. Prediction of total carotenoids, color and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rosalizan Md Saleh
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Industrial Crops Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang, Selangor Malaysia
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Barbara Sturm
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy(ATB) Max‐Eyth‐Allee 100 14469 Potsdam Germany
- Humboldt Universität zu Berlin Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences 10115 Berlin Germany
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Dong C, Yang C, Liu Z, Zhang R, Yan P, An T, Zhao Y, Li Y. Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging. SENSORS 2021; 21:s21238051. [PMID: 34884054 PMCID: PMC8659440 DOI: 10.3390/s21238051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/16/2021] [Accepted: 11/21/2021] [Indexed: 12/04/2022]
Abstract
Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.
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Affiliation(s)
- Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Chongshan Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Zhongyuan Liu
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Rentian Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Peng Yan
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Ting An
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Yan Zhao
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
- Correspondence: (Y.Z.); (Y.L.)
| | - Yang Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- Correspondence: (Y.Z.); (Y.L.)
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Su WH, Xue H. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods 2021; 10:2146. [PMID: 34574253 PMCID: PMC8472741 DOI: 10.3390/foods10092146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
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Affiliation(s)
- Wen-Hao Su
- Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing 100083, China;
| | - Huidan Xue
- School of Economics and Management, Beijing University of Technology, Beijing 100124, China
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Ren Y, Lin X, Lei T, Sun DW. Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit Rev Food Sci Nutr 2021; 62:4267-4293. [PMID: 34275402 DOI: 10.1080/10408398.2021.1947773] [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: 10/20/2022]
Abstract
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
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Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11114841] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.
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