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Dong X, Dong Y, Liu J, Wang C, Bao C, Wang N, Zhao X, Chen Z. Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 323:124938. [PMID: 39126863 DOI: 10.1016/j.saa.2024.124938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/07/2024] [Accepted: 08/04/2024] [Indexed: 08/12/2024]
Abstract
As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.
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Affiliation(s)
- Xinyi Dong
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Ying Dong
- Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China; Huangpu Customs Technology Center, Sanyuan Road 66, Dongguan 523000, China
| | - Jinming Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China.
| | - Chunqi Wang
- College of Food, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163319, China
| | - Changhao Bao
- College of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163319, China
| | - Na Wang
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Xiaoyu Zhao
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Zhengguang Chen
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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Zhang T, Wang Y, Sun J, Liang J, Wang B, Xu X, Xu J, Liu L. Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124112. [PMID: 38518439 DOI: 10.1016/j.saa.2024.124112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 03/24/2024]
Abstract
Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images - EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.
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Affiliation(s)
- Tianrui Zhang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Yifan Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jiansong Sun
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jing Liang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Bin Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Xiaoxuan Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Yunnan Research Institute, Nankai University, Kunming 650091, China
| | - Jing Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Lei Liu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
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Song C, Xie K, Chen H, Xu S, Mao H. Wheat ESCRT-III protein TaSAL1 regulates male gametophyte transmission and controls tillering and heading date. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:2372-2384. [PMID: 38206130 DOI: 10.1093/jxb/erae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/10/2024] [Indexed: 01/12/2024]
Abstract
Charged multivesicular protein 1 (CHMP1) is a member of the endosomal sorting complex required for transport-III (ESCRT-III) complex that targets membrane localized signaling receptors to intralumenal vesicles in the multivesicular body of the endosome and eventually to the lysosome for degradation. Although CHMP1 plays roles in various plant growth and development processes, little is known about its function in wheat. In this study, we systematically analysed the members of the ESCRT-III complex in wheat (Triticum aestivum) and found that their orthologs were highly conserved in eukaryotic evolution. We identified CHMP1 homologous genes, TaSAL1s, and found that they were constitutively expressed in wheat tissues and essential for plant reproduction. Subcellular localization assays showed these proteins aggregated with and closely associated with the endoplasmic reticulum when ectopically expressed in tobacco leaves. We also found these proteins were toxic and caused leaf death. A genetic and reciprocal cross analysis revealed that TaSAL1 leads to defects in male gametophyte biogenesis. Moreover, phenotypic and metabolomic analysis showed that TaSAL1 may regulate tillering and heading date through phytohormone pathways. Overall, our results highlight the role of CHMP1 in wheat, particularly in male gametophyte biogenesis, with implications for improving plant growth and developing new strategies for plant breeding and genetic engineering.
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Affiliation(s)
- Chengxiang Song
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Kaidi Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Shuhao Xu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
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Wang Y, Ou X, He HJ, Kamruzzaman M. Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem X 2024; 21:101235. [PMID: 38420503 PMCID: PMC10900407 DOI: 10.1016/j.fochx.2024.101235] [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: 12/08/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.
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Affiliation(s)
- Yuling Wang
- 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
| | - 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
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Cheng R, Bai X, Guo J, Huang L, Zhao D, Liu Z, Zhang W. Hyperspectral discrimination of ginseng variety and age from Changbai Mountain area. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123613. [PMID: 37976570 DOI: 10.1016/j.saa.2023.123613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/12/2023] [Accepted: 11/01/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND The efficacy and market value of Panax ginseng Meyer are significantly influenced by its diversity and age. Traditional identification methods are prone to subjective biases and necessitate the use of destructive sample processing, leading to the loss and wastage of ginseng. Consequently, non-destructive in-situ identification has emerged as a crucial subject of interest for both researchers and the ginseng industry. The advancement of technology and the expansion of research have introduced spectral technology and image processing technology as novel approaches and concepts for non-destructive in-situ identification. METHODS Hyperspectral imaging (HSI) is a methodology that combines conventional spectroscopy and imaging to acquire comprehensive spectral and spatial data from various samples. In this study, we investigated the use of Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifier algorithms, in conjunction with HSI classification technology, for quasi-Artificial Intelligence (quasi-AI) ginseng identification. To enhance the hyperspectral images prior to SVM classification, we compared the efficacy of Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). RESULTS The classification of ginseng based on age was accomplished through the utilization of Radial Basis Function (RBF) kernel SVM and SAM algorithm, which was trained on feature enhanced images. The classification of WMG, MCG, and GG is primarily based on age, with the endmember spectrum serving as the foundation for SAM and SVM. CONCLUSION The "endmember spectrum set" derived from the classification outcomes can serve as the "mutation point" for identifying ginseng of different ages.
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Affiliation(s)
- Ruiyang Cheng
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Xueyuan Bai
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jianying Guo
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Luqi Huang
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Daqing Zhao
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Zhaojian Liu
- Department of Cell Biology, School of Basic Medical Science, Shandong University, Jinan, China.
| | - Wei Zhang
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China.
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Jiang D, Wang K, Li H, Zhang Y. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign. SENSORS (BASEL, SWITZERLAND) 2024; 24:1245. [PMID: 38400402 PMCID: PMC10893441 DOI: 10.3390/s24041245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024]
Abstract
This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.
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Affiliation(s)
- Dapeng Jiang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Keqi Wang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Hongbo Li
- College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yizhuo Zhang
- College of Computer Science and Artificial Intelligence, Changzhou University, 1 Gehu Middle Rd., Changzhou 213164, China
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Johnson NAN, Adade SYSS, Haruna SA, Ekumah JN, Ma Y. Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123623. [PMID: 37989004 DOI: 10.1016/j.saa.2023.123623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/17/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023]
Abstract
The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.
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Affiliation(s)
- Nana Adwoa Nkuma Johnson
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China
| | - Selorm Yao-Say Solomon Adade
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Dietetics, Ho Teaching Hospital, Ho, Ghana.
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Food Science and Technology, Kano University of Science andTechnology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Food Science, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Ghana
| | - Yongkun Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China.
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Fusè M, Mazzeo G, Bloino J, Longhi G, Abbate S. Pushing measurements and interpretation of VCD spectra in the IR, NIR and visible ranges to the detectability and computational complexity limits. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123496. [PMID: 37832448 DOI: 10.1016/j.saa.2023.123496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/29/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023]
Abstract
(R)-Limonene VCD and IR absorption spectra for neat liquid samples are considered from 900 to 16,000 cm-1, using mostly data by Nafie et al. up to 10,000 cm-1 and from previous investigations of the Brescia group. New VCD data are recorded in the merely overtone and combination region between 1800 and 2400 cm-1 and for the Δn= 6 overtone CH-stretching region above 15,000 cm-1. The GVPT2 anharmonic DFT calculations permit satisfactory interpretation of the fundamental + overtone/combination of deformation modes in the mid-IR up to 3500 cm-1. The GVPT2 approach is also used for the first CH-stretching overtone regions together with their combination with deformation modes up to 9000 cm-1. Then the local-mode approach developed within the DFT protocol is employed in all the CH-stretching regions (fundamental + overtones) and is found to satisfactorily account for the observed spectra, justifying the constant VCD pattern observed for all overtones. On the basis of the local-mode model the components of the bisignate VCD spectrum are attributed to the stretchings of the axial and equatorial CH bonds in α-position with respect to the ring CC double bond.
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Affiliation(s)
- Marco Fusè
- Dipartimento di Medicina Molecolare e Traslazionale, Università di Brescia, Viale Europa 11, 25123, Brescia, Italy
| | - Giuseppe Mazzeo
- Dipartimento di Medicina Molecolare e Traslazionale, Università di Brescia, Viale Europa 11, 25123, Brescia, Italy
| | - Julien Bloino
- Scuola Normale Superiore, Piazza dei Cavalieri, 56125, Pisa, Italy
| | - Giovanna Longhi
- Dipartimento di Medicina Molecolare e Traslazionale, Università di Brescia, Viale Europa 11, 25123, Brescia, Italy; Istituto Nazionale di Ottica (INO), CNR, Research Unit of Brescia, c/o CSMT, VIA Branze 45, 25123, Brescia, Italy
| | - Sergio Abbate
- Dipartimento di Medicina Molecolare e Traslazionale, Università di Brescia, Viale Europa 11, 25123, Brescia, Italy; Istituto Nazionale di Ottica (INO), CNR, Research Unit of Brescia, c/o CSMT, VIA Branze 45, 25123, Brescia, Italy.
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Ciaccheri L, De Girolamo A, Cervellieri S, Lippolis V, Mencaglia AA, Pascale M, Mignani AG. Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran. Molecules 2023; 28:7808. [PMID: 38067538 PMCID: PMC10708224 DOI: 10.3390/molecules28237808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40-1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.
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Affiliation(s)
- Leonardo Ciaccheri
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
| | - Annalisa De Girolamo
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Salvatore Cervellieri
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Vincenzo Lippolis
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Andrea Azelio Mencaglia
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
| | - Michelangelo Pascale
- CNR—Istituto di Scienze dell’Alimentazione (ISA), Via Roma, 64, 83100 Avellino, Italy;
| | - Anna Grazia Mignani
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
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Boglou V, Verginadis D, Karlis A. Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8476. [PMID: 37896569 PMCID: PMC10610992 DOI: 10.3390/s23208476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/08/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.
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Affiliation(s)
| | | | - Athanasios Karlis
- Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), 67100 Xanthi, Greece; (V.B.); (D.V.)
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Pirutin SK, Jia S, Yusipovich AI, Shank MA, Parshina EY, Rubin AB. Vibrational Spectroscopy as a Tool for Bioanalytical and Biomonitoring Studies. Int J Mol Sci 2023; 24:ijms24086947. [PMID: 37108111 PMCID: PMC10138916 DOI: 10.3390/ijms24086947] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.
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Affiliation(s)
- Sergey K Pirutin
- Faculty of Biology, Shenzhen MSU-BIT University, No. 1, International University Park Road, Dayun New Town, Longgang District, Shenzhen 518172, China
- Faculty of Biology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
- Institute of Theoretical and Experimental Biophysics of Russian Academy of Sciences, Institutskaya St. 3, 142290 Pushchino, Russia
| | - Shunchao Jia
- Faculty of Biology, Shenzhen MSU-BIT University, No. 1, International University Park Road, Dayun New Town, Longgang District, Shenzhen 518172, China
| | - Alexander I Yusipovich
- Faculty of Biology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Mikhail A Shank
- Faculty of Biology, Shenzhen MSU-BIT University, No. 1, International University Park Road, Dayun New Town, Longgang District, Shenzhen 518172, China
- Faculty of Biology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Evgeniia Yu Parshina
- Faculty of Biology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Andrey B Rubin
- Faculty of Biology, Shenzhen MSU-BIT University, No. 1, International University Park Road, Dayun New Town, Longgang District, Shenzhen 518172, China
- Faculty of Biology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
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Martínez-Martín I, Hernández-Jiménez M, Revilla I, Vivar-Quintana AM. Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:1491. [PMID: 36772530 PMCID: PMC9920201 DOI: 10.3390/s23031491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.
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Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review. Foods 2022; 12:foods12010132. [PMID: 36613348 PMCID: PMC9818947 DOI: 10.3390/foods12010132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/06/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
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Affiliation(s)
- Weixin Ye
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Jingkun Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
- Correspondence: (P.G.); (C.Z.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
- Correspondence: (P.G.); (C.Z.)
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