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Ong P, Yeh CW, Tsai IL, Lee WJ, Wang YJ, Chuang YK. Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123214. [PMID: 37531681 DOI: 10.1016/j.saa.2023.123214] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023]
Abstract
Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.
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Affiliation(s)
- Pauline Ong
- Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
| | - Ching-Wen Yeh
- Master's Program in Food Safety, College of Nutrition, Taipei Medical University, 250 Wusing Street, Taipei 11031, Taiwan.
| | - I-Lin Tsai
- Department of Biochemistry and Molecular Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
| | - Wei-Ju Lee
- School of Food Safety, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan.
| | - Yu-Jen Wang
- Department of Radiation Oncology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan; School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
| | - Yung-Kun Chuang
- Master's Program in Food Safety, College of Nutrition, Taipei Medical University, 250 Wusing Street, Taipei 11031, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan.
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2
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Fatemi A, Singh V, Kamruzzaman M. Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy. Food Chem 2022; 383:132442. [PMID: 35182865 DOI: 10.1016/j.foodchem.2022.132442] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 11/19/2022]
Abstract
Many studies have been conducted using NIR spectroscopy to predict corn constituents; however, a systematic investigation of the spectral sub-regions under the scope of overtones and combinations has not been performed. In this study, the corn spectra were divided into second overtones (1100-1388 nm), first overtones (1390-1852 nm), and combinations (1852-2498 nm). Then, using variable importance in projection and genetic algorithm, each region was inspected sequentially to identify the most informative sub-region for each attribute to improve interpretability. The identified spectral subsets were further tuned to select the most influential bands for each attribute. The sub-regions in combinations bands was most informative for predicting water (1908-2108 nm, 2 bands), oil (2176-2304 nm, 6 bands), and protein (2130-2190 nm, 3 bands), whereas the first overtones region was the best for predicting starch (1452-1770 nm, 5 bands). Results provided valuable information for potential hardware and software improvements.
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Affiliation(s)
- Ali Fatemi
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Vijay Singh
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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Chen MJ, Yin HL, Liu Y, Wang RR, Jiang LW, Li P. Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:114-124. [PMID: 34913444 DOI: 10.1039/d1ay01634b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.
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Affiliation(s)
- Meng-Juan Chen
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Han-Liang Yin
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Yang Liu
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Rong-Rong Wang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Li-Wen Jiang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Pao Li
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
- Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, P. R. China
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4
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Keskin M, Arslan A, Soysal Y, Sekerli YE, Celiktas N. Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15846] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Muharrem Keskin
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Aysel Arslan
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Yurtsever Soysal
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Yunus Emre Sekerli
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Nafiz Celiktas
- Department of Field Crops Faculty of Agriculture Hatay Mustafa Kemal University Antakya, Hatay Turkey
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5
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Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06094-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Du M, Liu X, Wang D, Yang Q, Duan A, Chen H, Liu Y, Wang Q, Ni BJ. Understanding the fate and impact of capsaicin in anaerobic co-digestion of food waste and waste activated sludge. WATER RESEARCH 2021; 188:116539. [PMID: 33125995 DOI: 10.1016/j.watres.2020.116539] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
Anaerobic co-digestion is an attractive option to treat food waste and waste activated sludge, which is increasingly applied in real-world situations. As an active component in Capsicum species being substantially present in food waste in many areas, capsaicin has been recently demonstrated to inhibit the anaerobic co-digestion. However, the interaction between capsaicin and anaerobic co-digestion are still poorly understood. This work therefore aims to deeply understand the fate and impact of capsaicin in the anaerobic co-digestion. Experiment results showed that capsaicin was completely degraded in anaerobic co-digestion by hydroxylation, O-demethylation, dehydrogenation and doubly oxidization, respectively. Although methane was proven to be produced from capsaicin degradation, the increase in capsaicin concentration resulted in decrease in methane yield from the anaerobic co-digestion. With an increase of capsaicin from 2 ± 0.7 to 68 ± 4 mg/g volatile solids (VS), the maximal methane yield decreased from 274.6 ± 9.7 to 188.9 ± 8.4 mL/g VS. The mechanic investigations demonstrated that the presence of capsaicin induced apoptosis, probably by either altering key kinases or decreasing the intracellular NAD+/NADH ratio, which led to significant inhibitions to hydrolysis, acidogenesis, and methanogenesis, especially acetotrophic methanogenesis. Illumina Miseq sequencing analysis exhibited that capsaicin promoted the populations of complex organic degradation microbes such as Escherichia-Shigella and Fonticella but decreased the numbers of anaerobes relevant to hydrolysis, acidogenesis, and methanogenesis such as Bacteroide and Methanobacterium.
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Affiliation(s)
- Mingting Du
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, P.R. China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, P.R. China.
| | - Xuran Liu
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, P.R. China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, P.R. China
| | - Dongbo Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, P.R. China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, P.R. China.
| | - Qi Yang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, P.R. China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, P.R. China
| | - Abing Duan
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, P.R. China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, P.R. China.
| | - Hong Chen
- Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha 410004, China
| | - Yiwen Liu
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Qilin Wang
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Bing-Jie Ni
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
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da Silva Antonio A, Wiedemann LSM, da Veiga Junior VF. Food Pungency: the Evolution of Methods for Capsaicinoid Analysis. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01470-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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8
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Hwang IM, Moon EW, Lee HW, Jamila N, Su Kim K, Ha JH, Kim SH. Discrimination of Chili Powder Origin Using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES), and Near Infrared (NIR) Spectroscopy. ANAL LETT 2018. [DOI: 10.1080/00032719.2018.1508293] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- In Min Hwang
- Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju, Republic of Korea
- Department of Food and Nutrition, Chosun University, Gwangju, Republic of Korea
| | - Eun Woo Moon
- Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju, Republic of Korea
| | - Hae-Won Lee
- Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju, Republic of Korea
| | - Nargis Jamila
- Department of Chemistry, Shaheed Benazir Bhutto Women University, Peshawar, Pakistan
| | - Kyong Su Kim
- Department of Food and Nutrition, Chosun University, Gwangju, Republic of Korea
| | - Ji-Hyoung Ha
- Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju, Republic of Korea
| | - Sung Hyun Kim
- Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju, Republic of Korea
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