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Cui H, Gu F, Qin J, Li Z, Zhang Y, Guo Q, Wang Q. Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach. Foods 2024; 13:1722. [PMID: 38890950 PMCID: PMC11171514 DOI: 10.3390/foods13111722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/21/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.
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Affiliation(s)
- Haofan Cui
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Fengying Gu
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Jingjing Qin
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Zhenyuan Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Yu Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Beijing 100081, China;
| | - Qin Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Qiang Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [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: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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Kim J, Kurniawan H, Faqeerzada MA, Kim G, Lee H, Kim MS, Baek I, Cho BK. Proximate Content Monitoring of Black Soldier Fly Larval ( Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging. Food Sci Anim Resour 2023; 43:1150-1169. [PMID: 37969323 PMCID: PMC10636226 DOI: 10.5851/kosfa.2023.e33] [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: 05/17/2023] [Revised: 06/26/2023] [Accepted: 07/02/2023] [Indexed: 11/17/2023] Open
Abstract
Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.
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Affiliation(s)
- Juntae Kim
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Hary Kurniawan
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Geonwoo Kim
- Department of Bio-Industrial Machinery
Engineering, College of Agriculture and Life Science, Gyeongsang National
University, Jinju 52828, Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering,
College of Agriculture, Life & Environment Science, Chungbuk National
University, Cheongju 28644, Korea
| | - Moon Sung Kim
- Environmental Microbial and Food Safety
Laboratory, Agricultural Research Service, United States Department of
Agriculture, Beltsville, MD 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety
Laboratory, Agricultural Research Service, United States Department of
Agriculture, Beltsville, MD 20705, USA
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
- Department of Smart Agriculture Systems,
College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
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Wunderlich P, Pauli D, Neumaier M, Wisser S, Danneel HJ, Lohweg V, Dörksen H. Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors. Foods 2023; 12:foods12061347. [PMID: 36981272 PMCID: PMC10048631 DOI: 10.3390/foods12061347] [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: 03/01/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The waste of food presents a challenge for achieving a sustainable world. In Germany alone, over 10 million tonnes of food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.
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Affiliation(s)
- Paul Wunderlich
- inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Daniel Pauli
- Institute for Life Science Technologies (ILT.NRW), OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Michael Neumaier
- Institute for Life Science Technologies (ILT.NRW), OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Stephanie Wisser
- inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Hans-Jürgen Danneel
- Institute for Life Science Technologies (ILT.NRW), OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Volker Lohweg
- inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
| | - Helene Dörksen
- inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
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Wu Q, Mousa MA, Al-qurashi AD, Ibrahim OH, Abo-Elyousr KA, Rausch K, Abdel Aal AM, Kamruzzaman M. Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Curr Res Food Sci 2023; 6:100483. [PMID: 37033735 PMCID: PMC10073987 DOI: 10.1016/j.crfs.2023.100483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.
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Authenticity of almond flour using handheld near infrared instruments and one class classifiers. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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Jang KE, Kim G, Shin MH, Cho JG, Jeong JH, Lee SK, Kang D, Kim JG. Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in ‘Madoka’ Peaches. PLANTS 2022; 11:plants11172327. [PMID: 36079708 PMCID: PMC9460469 DOI: 10.3390/plants11172327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.
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Affiliation(s)
- Kyeong Eun Jang
- Division of Applied Life Science, Graduate School of Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Geonwoo Kim
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Mi Hee Shin
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jung Gun Cho
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Jae Hoon Jeong
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Seul Ki Lee
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Dongyoung Kang
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jin Gook Kim
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Department of Horticulture, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Correspondence:
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Zheng L, Bao Q, Weng S, Tao J, Zhang D, Huang L, Zhao J. Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120813. [PMID: 34998050 DOI: 10.1016/j.saa.2021.120813] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.
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Affiliation(s)
- Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.
| | - Qian Bao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jianpeng Tao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
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Panda BK, Mishra G, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110889] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Tunny SS, Amanah HZ, Faqeerzada MA, Wakholi C, Kim MS, Baek I, Cho BK. Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables. SENSORS (BASEL, SWITZERLAND) 2022; 22:1775. [PMID: 35270921 PMCID: PMC8914723 DOI: 10.3390/s22051775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/08/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.
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Affiliation(s)
- Salma Sultana Tunny
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Hanim Z. Amanah
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
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12
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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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