1
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Li H, Jiang D, Dong W, Cheng J, Zhang X. Towards Sustainable and Dynamic Modeling Analysis in Korean Pine Nuts: An Online Learning Approach with NIRS. Foods 2024; 13:2857. [PMID: 39272621 PMCID: PMC11394716 DOI: 10.3390/foods13172857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/15/2024] Open
Abstract
Due to its advantages such as speed and noninvasive nature, near-infrared spectroscopy (NIRS) technology has been widely used in detecting the nutritional content of nut food. This study aims to address the problem of offline quantitative analysis models producing unsatisfactory results for different batches of samples due to complex and unquantifiable factors such as storage conditions and origin differences of Korean pine nuts. Based on the offline model, an online learning model was proposed using recursive partial least squares (RPLS) regression with online multiplicative scatter correction (OMSC) preprocessing. This approach enables online updates of the original detection model using a small amount of sample data, thereby improving its generalization ability. The OMSC algorithm reduces the prediction error caused by the inability to perform effective scatter correction on the updated dataset. The uninformative variable elimination (UVE) algorithm appropriately increases the number of selected feature bands during the model updating process to expand the range of potentially relevant features. The final model is iteratively obtained by combining new sample feature data with RPLS. The results show that, after OMSC preprocessing, with the number of features increased to 100, the new online model's R2 value for the prediction set is 0.8945. The root mean square error of prediction (RMSEP) is 3.5964, significantly outperforming the offline model, which yields values of 0.4525 and 24.6543, respectively. This indicates that the online model has dynamic and sustainable characteristics that closely approximate practical detection, and it provides technical references and methodologies for the design and development of detection systems. It also offers an environmentally friendly tool for rapid on-site analysis for nut food regulatory agencies and production enterprises.
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Affiliation(s)
- Hongbo Li
- College of Electrical and Information, Northeast Agricultural University, 59 Changjiang Rd., Harbin 150030, China
| | - Dapeng Jiang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China
| | - Wanjing Dong
- College of Economics and Management, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China
| | - Jin Cheng
- College of Electrical and Information, Northeast Agricultural University, 59 Changjiang Rd., Harbin 150030, China
| | - Xihai Zhang
- College of Electrical and Information, Northeast Agricultural University, 59 Changjiang Rd., Harbin 150030, China
- National Key Laboratory of Smart Farm Technology and System, Northeast Agricultural University, 59 Changjiang Rd., Harbin 150030, China
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2
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Cebi N, Bekiroglu H, Erarslan A. Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening. Molecules 2023; 28:7933. [PMID: 38067662 PMCID: PMC10707828 DOI: 10.3390/molecules28237933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.
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Affiliation(s)
- Nur Cebi
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
| | - Hatice Bekiroglu
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
- Food Engineering Department, Faculty of Agriculture, Sirnak University, 73300 Sirnak, Turkey
| | - Azime Erarslan
- Bioengineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey;
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3
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [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: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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4
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Freitag S, Sulyok M, Logan N, Elliott CT, Krska R. The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops. Compr Rev Food Sci Food Saf 2022; 21:5199-5224. [PMID: 36215130 DOI: 10.1111/1541-4337.13054] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/18/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.
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Affiliation(s)
- Stephan Freitag
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Michael Sulyok
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria
| | - Natasha Logan
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Rudolf Krska
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna, Tulln, Austria.,FFoQSI GmbH-Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, Austria.,Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland, UK
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5
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Zhang C, Huang W, Liang X, He X, Tian X, Chen L, Wang Q. Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding. FRONTIERS IN PLANT SCIENCE 2022; 13:956636. [PMID: 36186064 PMCID: PMC9520625 DOI: 10.3389/fpls.2022.956636] [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: 05/31/2022] [Accepted: 07/28/2022] [Indexed: 06/16/2023]
Abstract
Slight crack of cottonseed is a critical factor influencing the germination rate of cotton due to foamed acid or water entering cottonseed through testa. However, it is very difficult to detect cottonseed with slight crack using common non-destructive detection methods, such as machine vision, optical spectroscopy, and thermal imaging, because slight crack has little effect on morphology, chemical substances or temperature. By contrast, the acoustic method shows a sensitivity to fine structure defects and demonstrates potential application in seed detection. This paper presents a novel method to detect slightly cracked cottonseed using air-coupled ultrasound with a light-weight vision transformer (ViT) and a sound-to-image encoding method. The echo signal of air-coupled ultrasound from cottonseed is obtained by non-contact and non-destructive methods. The intrinsic mode functions (IMFs) of ultrasound signal are obtained as the sound features using variational mode decomposition (VMD) approach. Then the sound features are converted into colorful images by a color encoding method. This method uses different colored lines to represent the changes of different values of IMFs according to the specified encoding period. A light-weight MobileViT method is utilized to identify the slightly cracked cottonseeds using encoding colorful images corresponding to cottonseeds. The experimental results show an average overall recognition accuracy of 90.7% for slightly cracked cottonseed from normal cottonseed, which indicates that the proposed method is reliable to applications in detection task of cottonseed with slight crack.
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Affiliation(s)
- Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xiaoting Liang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Information Technology, Shanghai Ocean University, Shanghai, China
| | - Xin He
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Liping Chen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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6
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Hongfei Z, Lianhe Y, Wangkun D, Zhongzhi H. Pixel-level rapid detection of Aflatoxin B1 based on 1D-modified temporal convolutional network and hyperspectral imaging. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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7
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Detection of Aflatoxin B1 in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies. SENSORS 2022; 22:s22134864. [PMID: 35808359 PMCID: PMC9269126 DOI: 10.3390/s22134864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022]
Abstract
To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB1 quantitatively.
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8
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Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food. Foods 2022; 11:foods11070930. [PMID: 35407017 PMCID: PMC8997473 DOI: 10.3390/foods11070930] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.
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9
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Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3030045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.
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10
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Song H, Li F, Guang P, Yang X, Pan H, Huang F. Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models. J Food Prot 2021; 84:1315-1320. [PMID: 33710323 DOI: 10.4315/jfp-20-447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
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Affiliation(s)
- Han Song
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Feng Li
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Peiwen Guang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Xinhao Yang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Huanyu Pan
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
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11
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Wang Z, Fan S, Wu J, Zhang C, Xu F, Yang X, Li J. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 254:119666. [PMID: 33744703 DOI: 10.1016/j.saa.2021.119666] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/28/2021] [Accepted: 02/28/2021] [Indexed: 05/28/2023]
Abstract
Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is Rpre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.
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Affiliation(s)
- Zheli Wang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Jingzhu Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Chi Zhang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Fengying Xu
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
| | - Jiangbo Li
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
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12
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Tittlemier S, Cramer B, Dall’Asta C, Iha M, Lattanzio V, Maragos C, Solfrizzo M, Stranska M, Stroka J, Sumarah M. Developments in mycotoxin analysis: an update for 2018-19. WORLD MYCOTOXIN J 2020. [DOI: 10.3920/wmj2019.2535] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
This review summarises developments on the analysis of various matrices for mycotoxins that have been published in the period from mid-2018 to mid-2019. Analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes, and zearalenone are covered in individual sections. Advances in sampling strategies are also discussed in a dedicated section. In addition, developments in multi-mycotoxin methods – including comprehensive mass spectrometric-based methods as well as simple immunoassays – are also reviewed. This critical review aims to briefly present the most important recent developments and trends in mycotoxin determination as well as to address limitations of the presented methodologies.
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Affiliation(s)
- S.A. Tittlemier
- Canadian Grain Commission, Grain Research Laboratory, Winnipeg, MB, R3C 3G8, Canada
| | - B. Cramer
- University of Münster, Institute of Food Chemistry, Corrensstr. 45, 48149 Münster, Germany
| | - C. Dall’Asta
- Università di Parma, Department of Food and Drug, Viale delle Scienze 23/A, 43124 Parma, Italy
| | - M.H. Iha
- Adolfo Lutz Institute of Ribeirão Preto, CEP 14085-410, Ribeirão Preto-SP, Brazil
| | - V.M.T. Lattanzio
- National Research Council of Italy, Institute of Sciences of Food Production, via Amendola 122/O, 70126 Bari, Italy
| | - C. Maragos
- United States Department of Agriculture, ARS National Center for Agricultural Utilization Research, Peoria, IL 61604, USA
| | - M. Solfrizzo
- National Research Council of Italy, Institute of Sciences of Food Production, via Amendola 122/O, 70126 Bari, Italy
| | - M. Stranska
- Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - J. Stroka
- European Commission, Joint Research Centre, 2440 Geel, Belgium
| | - M. Sumarah
- Agriculture and Agri-Food Canada, London Research and Development Centre, London, ON, N5V 4T3, Canada
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