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Conceição RRP, Queiroz VAV, Medeiros EP, Araújo JB, Araújo DDS, Miguel RA, Stoianoff MAR, Simeone MLF. Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods. BRAZ J BIOL 2024; 84:e277974. [PMID: 38808784 DOI: 10.1590/1519-6984.277974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 04/11/2024] [Indexed: 05/30/2024] Open
Abstract
Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.
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Affiliation(s)
- R R P Conceição
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, MG, Brasil
| | | | | | - J B Araújo
- Embrapa Algodão, Campina Grande, PB, Brasil
| | | | - R A Miguel
- Embrapa Milho e Sorgo, Sete Lagoas, MG, Brasil
| | - M A R Stoianoff
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, MG, Brasil
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Yang C, Sun X, Wang J, Lv H, Dong P, Xi L, Shi L. YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection. PeerJ Comput Sci 2024; 10:e1948. [PMID: 38660210 PMCID: PMC11041926 DOI: 10.7717/peerj-cs.1948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/29/2024] [Indexed: 04/26/2024]
Abstract
Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.
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Affiliation(s)
- Chengkai Yang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xiaoyun Sun
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Jian Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Haiyan Lv
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Ping Dong
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Lei Xi
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
| | - Lei Shi
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Grain Crop Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, Henan, China
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Yan X, Liu S, Wang S, Cui J, Wang Y, Lv Y, Li H, Feng Y, Luo R, Zhang Z, Zhang L. Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging. Foods 2024; 13:424. [PMID: 38338559 PMCID: PMC10855435 DOI: 10.3390/foods13030424] [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: 11/17/2023] [Revised: 12/26/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.
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Affiliation(s)
- Xiuwei Yan
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Sijia Liu
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Songlei Wang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Jiarui Cui
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yongrui Wang
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yu Lv
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Hui Li
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Yingjie Feng
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Ruiming Luo
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Zhifeng Zhang
- College of Aquaculture, Huazhong Agricultural University, Wuhan 430070, China;
| | - Lei Zhang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
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Mohamed AB, Gathman RJ, Chavez RA, Wagacha MJ, Mutegi CK, Muthomi JW, Stasiewicz MJ. Multispectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels. J Food Prot 2023; 86:100142. [PMID: 37562513 DOI: 10.1016/j.jfp.2023.100142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/12/2023]
Abstract
Fusarium species infect maize crops leading to the production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further postharvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multispectral sorting technique to analyze the reduction of fumonisin and toxigenic Fusarium species found contaminating maize kernels in Kenya. Maize samples (n = 99) were collected from six mycotoxin hotspot counties in Kenya (Embu, Meru, Tharaka Nithi, Machakos, Makueni, and Kitui County) and analyzed for aflatoxin and fumonisin using commercial ELISA kits. Aflatoxin levels in majority (91%) of the samples were below the 10 ng/g threshold set by the Kenya Bureau of Standards and therefore not studied further. The 23/99 samples that had >2,000 ng/g of fumonisin were selected for sorting. The sorter was calibrated using kernels sourced from Ghana to reject visibly high-risk kernels for fumonisin contamination using reflectance at nine distinct wavelengths (470-1,550 nm). Accepted and rejected streams were tested for fumonisin using ELISA, and the presence of toxigenic Fusarium using qPCR. After sorting, there was a significant (p < 0.001) reduction of fumonisin, by an average of 1.8 log ng/g (98%) and ranging between 0.14 and 2.7 log ng/g reduction (28-99.8%) with a median mass rejection rate of 1.9% (ranged 0% to 48%). The fumonisin rejection rate ranged between 0 and 99.8% with a median of 77%. There was also a significant reduction (p = 0.005) in the proportion of DNA represented by toxigenic Fusarium, from a mean of 30-1.4%. This study demonstrates the use of multispectral sorting as a potential postharvest intervention tool for the reduction of Fusarium species and preformed fumonisin. The spectral sorting approach of this study suggests that classification algorithms based on high-risk visual features associated with mycotoxin can be applied across different sources of maize to reduce fumonisin.
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Affiliation(s)
- Asha B Mohamed
- Department of Plant Science and Crop Protection, University of Nairobi, P. O. Box 29053-00625, Nairobi, Kenya
| | - Rachel J Gathman
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 1302 W Pennsylvania Ave., Urbana, IL 61801, USA
| | - Ruben A Chavez
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 1302 W Pennsylvania Ave., Urbana, IL 61801, USA
| | - Maina J Wagacha
- Department of Biology, University of Nairobi, P.O. Box 30197, GPO, Nairobi, Kenya
| | - Charity K Mutegi
- International Institute of Tropical Agriculture, ILRI, P.O Box 30709-00100, Nairobi, Kenya
| | - James W Muthomi
- Department of Plant Science and Crop Protection, University of Nairobi, P. O. Box 29053-00625, Nairobi, Kenya
| | - Matthew J Stasiewicz
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 1302 W Pennsylvania Ave., Urbana, IL 61801, USA.
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Deng Y, You L, Wang X, Wu W, Kuca K, Wu Q, Wei W. Deoxynivalenol: Emerging Toxic Mechanisms and Control Strategies, Current and Future Perspectives. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37437258 DOI: 10.1021/acs.jafc.3c02020] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Deoxynivalenol (DON) is the most frequently present mycotoxin contaminant in food and feed, causing a variety of toxic effects in humans and animals. Currently, a series of mechanisms involved in DON toxicity have been identified. In addition to the activation of oxidative stress and the MAPK signaling pathway, DON can activate hypoxia-inducible factor-1α, which further regulates reactive oxygen species production and cancer cell apoptosis. Noncoding RNA and signaling pathways including Wnt/β-catenin, FOXO, and TLR4/NF-κB also participate in DON toxicity. The intestinal microbiota and the brain-gut axis play a crucial role in DON-induced growth inhibition. In view of the synergistic toxic effect of DON and other mycotoxins, strategies to detect DON and control it biologically and the development of enzymes for the biodegradation of various mycotoxins and their introduction in the market are the current and future research hotspots.
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Affiliation(s)
- Ying Deng
- College of Life Science, Yangtze University, Jingzhou 434025, China
| | - Li You
- College of Physical Education and Health, Chongqing College of International Business and Economics, Chongqing 401520, China
| | - Xu Wang
- National Reference Laboratory of Veterinary Drug Residues and MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University (HZAU), Wuhan, Hubei 430070, China
| | - Wenda Wu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
- Department of Chemistry, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
| | - Kamil Kuca
- Department of Chemistry, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada 18071, Spain
| | - Qinghua Wu
- College of Life Science, Yangtze University, Jingzhou 434025, China
- Department of Chemistry, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
| | - Wei Wei
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
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Nadimi M, Hawley E, Liu J, Hildebrand K, Sopiwnyk E, Paliwal J. Enhancing traceability of wheat quality through the supply chain. Compr Rev Food Sci Food Saf 2023; 22:2495-2522. [PMID: 37078119 DOI: 10.1111/1541-4337.13150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/02/2023] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.
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Affiliation(s)
- Mohammad Nadimi
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - Jing Liu
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
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Teixido-Orries I, Molino F, Femenias A, Ramos AJ, Marín S. Quantification and classification of deoxynivalenol-contaminated oat samples by near-infrared hyperspectral imaging. Food Chem 2023; 417:135924. [PMID: 36934710 DOI: 10.1016/j.foodchem.2023.135924] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/17/2023]
Abstract
Deoxynivalenol (DON) is the most occurring mycotoxin in oat and oat-based products. Near-infrared hyperspectral imaging (NIR-HSI) has been proposed as a promising methodology for analysing DON contamination in the food industry. The present study aims to apply NIR-HSI for DON detection in oat kernels and to quantify and classify naturally DON-contaminated oat samples. Unground and ground oat samples were scanned by NIR-HSI before their DON content was determined by HPLC. The data were pre-treated and analysed by PLS regression and four classification methods. The most efficient DON prediction model was for unground samples (R2 = 0.75 and RMSEP = 403.18 μg/kg), using twelve characteristic wavelengths with a special interest in 1203 and 1388 nm. The random forest algorithm of unground samples according to the EU maximum limit for unprocessed oats (1750 μg/kg) achieved a classification accuracy of 77.8 %. These findings indicate that NIR-HSI is a promising tool for detecting DON in oats.
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Affiliation(s)
- Irene Teixido-Orries
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Francisco Molino
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Antoni Femenias
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XIA, AGROTECNIO-CERCA Centre, Av. Rovira Roure 191, 25198 Lleida, Spain
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Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management. Toxins (Basel) 2023; 15:toxins15030192. [PMID: 36977083 PMCID: PMC10053988 DOI: 10.3390/toxins15030192] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.
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Fan KJ, Liu BY, Su WH. Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:2668. [PMID: 36904871 PMCID: PMC10007200 DOI: 10.3390/s23052668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.
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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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11
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Che S, Du G, Zhong X, Mo Z, Wang Z, Mao Y. Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0012. [PMID: 37040513 PMCID: PMC10076050 DOI: 10.34133/plantphenomics.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 06/19/2023]
Abstract
Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.
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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Xuefeng Zhong
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhendong Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572002, China
- Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, 572025, China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266073, China
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12
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Gao B, Lu W, Jin M, Chen Y. Non-targeted metabolomics of moldy wheat by ultra-performance liquid chromatography - quadrupole time-of-flight mass spectrometry. Front Microbiol 2023; 14:1136516. [PMID: 37089557 PMCID: PMC10119584 DOI: 10.3389/fmicb.2023.1136516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/28/2023] [Indexed: 04/25/2023] Open
Abstract
Introduction As one of the staple foods for the world's major populations, the safety of wheat is critical in ensuring people's wellbeing. However, mildew is one of the prevalent safety issues that threatens the quality of wheat during growth, production, and storage. Due to the complex nature of the microbial metabolites, the rapid identification of moldy wheat is challenging. Methods In this research, identification of moldy wheat samples was studied using ultra-performance liquid chromatography - quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with chemometrics. The non-targeted PCA model for identifying moldy wheat from normal wheat was established by using previously established compounds database of authentic wheat samples. The partial least squares-discriminant analysis (PLS-DA) was performed. Results and discussion By optimizing the model parameters, correct discrimination of the moldy wheat as low as 5% (w/w) adulteration level could be achieved. Differential biomarkers unique to moldy wheat were also extracted to identify between the moldy and authentic wheat samples. The results demonstrated that the chemical information of wheat combined with the existing PCA model could efficiently discriminate between the constructed moldy wheat samples. The study offered an effective method toward screening wheat safety.
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13
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Tan L, Li Q, Sun C, Li W, Tang N, Tang K. An Efficient HPLC-PDA Coupled With Supel™ Tox DON SPE Approach for the Analysis of Deoxynivalenol Contamination in Cereal Grains and Feedstuffs in Jiangxi Province. J Food Prot 2023; 86:100022. [PMID: 36916602 DOI: 10.1016/j.jfp.2022.100022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
Deoxynivalenol (DON) was commonly found in grains and feedstuffs, which can cause human chronic diseases. In this study, a quick and reliable method was developed for the determination of DON in grains and feedstuffs in Jiangxi Province market. The sample was extracted with acetonitrile-water (84:16, v/v), then purified by Supel™ Tox DON SPE column, and detected by high-performance liquid chromatography (HPLC). The results showed that the calibration curve of DON showed good linearity in the range of 0.01-10.0 μg/mL, and the correlation coefficient R2 = 0.9999. The recovery of DON in the spiked maize sample was 94.8-98.5% by spiking with DON at 0.2 µg/g, 1.0 µg/g, and 2.0 µg/g. The RSD was between 2.5 and 3.3%. This method was used to analyze 120 samples, including 90 grains and 30 feedstuffs, collected from the Jiangxi Province market. The results showed that 81 samples of grains were positive with 90.0% positive rates, and 30 samples of feedstuff were positive with 100% positive rates. Maximum concentration of DON was 0.7 μg/g in oat and 6.9 μg/g in wheat feedstuffs, respectively. Fortunately, the positive samples of grains were safe levels in comparison with National standards for food safety limits of mycotoxins in food (1.0 μg/g), while, the feedstuff of oats was over the Maximum Guideline Level of 16.7% (the Maximum Residue Limit, MRL is 5.0 μg/g). The results of this study about current DON pollution in the grains and feedstuffs on the Jiangxi Province market have not been previously reported.
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Affiliation(s)
- Lin Tan
- College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China; Agricultural and Rural Bureau of Tonggu County, Yichun 336200, People's Republic of China
| | - Qian Li
- College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China
| | - Chao Sun
- College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China
| | - Weiqiang Li
- College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China
| | - Ninan Tang
- Wuxi Professional College of Science and Technology, Wuxi 214028, People's Republic of China
| | - Kaijie Tang
- College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China.
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14
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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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15
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Deoxynivalenol: An Overview on Occurrence, Chemistry, Biosynthesis, Health Effects and Its Detection, Management, and Control Strategies in Food and Feed. MICROBIOLOGY RESEARCH 2022. [DOI: 10.3390/microbiolres13020023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Mycotoxins are fungi-produced secondary metabolites that can contaminate many foods eaten by humans and animals. Deoxynivalenol (DON), which is formed by Fusarium, is one of the most common occurring predominantly in cereal grains and thus poses a significant health risk. When DON is ingested, it can cause both acute and chronic toxicity. Acute signs include abdominal pain, anorexia, diarrhea, increased salivation, vomiting, and malaise. The most common effects of chronic DON exposure include changes in dietary efficacy, weight loss, and anorexia. This review provides a succinct overview of various sources, biosynthetic mechanisms, and genes governing DON production, along with its consequences on human and animal health. It also covers the effect of environmental factors on its production with potential detection, management, and control strategies.
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16
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Kumar P, Rani A, Singh S, Kumar A. Recent advances on
DNA
and omics‐based technology in Food testing and authentication: A review. J Food Saf 2022. [DOI: 10.1111/jfs.12986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Pramod Kumar
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Alka Rani
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Shalini Singh
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Anuj Kumar
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
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17
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Near infrared techniques applied to analysis of wheat-based products: Recent advances and future trends. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109115] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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18
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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19
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Yang D, Jiang J, Jie Y, Li Q, Shi T. Detection of the moldy status of the stored maize kernels using hyperspectral imaging and deep learning algorithms. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022. [DOI: 10.1080/10942912.2022.2027963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Dong Yang
- Academy of National Food and Strategic Reserves Administration, National Engineering Laboratory of Grain Storage and Logistics, Beijing, China
| | - Junyi Jiang
- Academy of National Food and Strategic Reserves Administration, National Engineering Laboratory of Grain Storage and Logistics, Beijing, China
| | - Yu Jie
- Academy of National Food and Strategic Reserves Administration, National Engineering Laboratory of Grain Storage and Logistics, Beijing, China
| | - Qianqian Li
- Academy of National Food and Strategic Reserves Administration, National Engineering Laboratory of Grain Storage and Logistics, Beijing, China
| | - Tianyu Shi
- Academy of National Food and Strategic Reserves Administration, National Engineering Laboratory of Grain Storage and Logistics, Beijing, China
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20
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Shen G, Cao Y, Yin X, Dong F, Xu J, Shi J, Lee YW. Rapid and nondestructive quantification of deoxynivalenol in individual wheat kernels using near-infrared hyperspectral imaging and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108420] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Cebrián E, Núñez F, Rodríguez M, Grassi S, González-Mohino A. Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System. Toxins (Basel) 2021; 13:620. [PMID: 34564624 PMCID: PMC8472122 DOI: 10.3390/toxins13090620] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/26/2021] [Accepted: 09/01/2021] [Indexed: 01/31/2023] Open
Abstract
The ripening process of dry-cured meat products is characterised by the development of fungi on the product's surface. This population plays a beneficial role, but, uncontrolled moulds represent a health risk, since some of them may produce mycotoxins, such as ochratoxin A (OTA). The aim of the present work is to assess the potential of near-infrared spectroscopy (NIRS) for the detection of OTA-producing mould species on dry-cured ham-based agar. The collected spectra were used to develop Support Vector Machines-Discriminant Analysis (SVM-DA) models by a hierarchical approach. Firstly, an SVM-DA model was tested to discriminate OTA and non-OTA producers; then, two models were tested to discriminate species among the OTA producers and the non-OTA producers. OTA and non-OTA-producing moulds were discriminated with 85% sensitivity and 86% specificity in the prediction. Furthermore, the SVM-DA model could differentiate non-OTA-producing species with a 95% sensitivity and specificity. Promising results were obtained for the prediction of the four OTA-producing species tested, with a 69% and 90% sensitivity and specificity, respectively. The preliminary approach demonstrated the high potential of NIR spectroscopy, coupled with Chemometrics, to be used as a real-time automated routine monitorization of dry-cured ham surfaces.
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Affiliation(s)
- Eva Cebrián
- Food Hygiene and Safety, Meat and Meat Products Research Institute (IProCar), Faculty of Veterinary Science, University of Extremadura, 10003 Cáceres, Spain; (E.C.); (F.N.); (M.R.)
| | - Félix Núñez
- Food Hygiene and Safety, Meat and Meat Products Research Institute (IProCar), Faculty of Veterinary Science, University of Extremadura, 10003 Cáceres, Spain; (E.C.); (F.N.); (M.R.)
| | - Mar Rodríguez
- Food Hygiene and Safety, Meat and Meat Products Research Institute (IProCar), Faculty of Veterinary Science, University of Extremadura, 10003 Cáceres, Spain; (E.C.); (F.N.); (M.R.)
| | - Silvia Grassi
- Department of Food, Environmental, and Nutritional Sciences (DeFENS), Università degli Studi di Milano, Via G. Celoria 2, 20133 Milan, Italy
| | - Alberto González-Mohino
- Food Technology, Meat and Meat Products Research Institute (IProCar), Faculty of Veterinary Science, University of Extremadura, 10003 Cáceres, Spain;
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22
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Fumagalli F, Ottoboni M, Pinotti L, Cheli F. Integrated Mycotoxin Management System in the Feed Supply Chain: Innovative Approaches. Toxins (Basel) 2021; 13:572. [PMID: 34437443 PMCID: PMC8402322 DOI: 10.3390/toxins13080572] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/13/2021] [Accepted: 08/13/2021] [Indexed: 12/24/2022] Open
Abstract
Exposure to mycotoxins is a worldwide concern as their occurrence is unavoidable and varies among geographical regions. Mycotoxins can affect the performance and quality of livestock production and act as carriers putting human health at risk. Feed can be contaminated by various fungal species, and mycotoxins co-occurrence, and modified and emerging mycotoxins are at the centre of modern mycotoxin research. Preventing mould and mycotoxin contamination is almost impossible; it is necessary for producers to implement a comprehensive mycotoxin management program to moderate these risks along the animal feed supply chain in an HACCP perspective. The objective of this paper is to suggest an innovative integrated system for handling mycotoxins in the feed chain, with an emphasis on novel strategies for mycotoxin control. Specific and selected technologies, such as nanotechnologies, and management protocols are reported as promising and sustainable options for implementing mycotoxins control, prevention, and management. Further research should be concentrated on methods to determine multi-contaminated samples, and emerging and modified mycotoxins.
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Affiliation(s)
- Francesca Fumagalli
- Department of Health, Animal Science and Food Safety, “Carlo Cantoni” University of Milan, 20134 Milan, Italy; (M.O.); (L.P.); (F.C.)
| | - Matteo Ottoboni
- Department of Health, Animal Science and Food Safety, “Carlo Cantoni” University of Milan, 20134 Milan, Italy; (M.O.); (L.P.); (F.C.)
| | - Luciano Pinotti
- Department of Health, Animal Science and Food Safety, “Carlo Cantoni” University of Milan, 20134 Milan, Italy; (M.O.); (L.P.); (F.C.)
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20134 Milan, Italy
| | - Federica Cheli
- Department of Health, Animal Science and Food Safety, “Carlo Cantoni” University of Milan, 20134 Milan, Italy; (M.O.); (L.P.); (F.C.)
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20134 Milan, Italy
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23
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Jorquera-Pereira D, Pavón-Pérez J, Ríos-Gajardo G. Identification of type B trichothecenes and zearalenone in Chilean cereals by planar chromatography coupled to mass spectroscopy. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2021; 38:1778-1787. [PMID: 34254899 DOI: 10.1080/19440049.2021.1948618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
High-performance thin-layer chromatography (HPTLC) and HPTLC coupled with mass spectrometry (MS) methods were described for the simultaneous determination of zearalenone (ZEA); type B trichothecenes (TCT-B); nivalenol (NIV) and deoxynivalenol (DON) along with its acetylated derivatives: 3-acetyldeoxynivalenol (3-ADON) and 15-acetyldeoxynivalenol (15-ADON). The extract samples were cleaned-up with Bond Elut Mycotoxin® solid-phase extraction cartridges. Then, separation was performed on HPTLC silica gel 60 F254 plates using toluene, ethyl acetate and formic acid (1:8:1 v/v/v) as mobile phase. Derivatisation was then performed with 10% aluminium trichloride in 50% methanol. Mycotoxin standards and spiked cereals grains were identified by UV spots at 366 nm, with retention factors (RF) of 0.20 (NIV), 0.39 (DON), 0.45 (15-ADON), 0.50 (3-ADON) and 0.60 (ZEA). Some parameters of validation were determined. Calibration data (n = 5) fitted a linear regression model with determination coefficients, R2 > 0.99. The recovery was determined in triplicate at two levels, ranging from 84.3 ± 2.2% to 114.2 ± 11.7%. Detection limits ranged from 80 to 120 μg kg-1 and quantification limits ranged from 120.0 to 200 μg kg-1. The analysis by HPTLC/electrospray (ESI)-MS in negative mode confirmed the presence of TCT-B and ZEA standards in Chilean cereals with mass signals at m/z 355, 371, 337, and 317 for DON, NIV, 3-ADON and 15-ADON, and ZEA, respectively.
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Affiliation(s)
- Diego Jorquera-Pereira
- Department of Food Science and Technology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile.,Interdisciplinary Group of Marine Biotechnology (GIBMAR), Center for Biotechnology, University of Concepcion, Concepcion, Chile.,Interdisciplinary Research Laboratory in Mycotoxins, Department of Food Science and Technology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile
| | - Jessy Pavón-Pérez
- Interdisciplinary Group of Marine Biotechnology (GIBMAR), Center for Biotechnology, University of Concepcion, Concepcion, Chile
| | - Gisela Ríos-Gajardo
- Department of Food Science and Technology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile.,Interdisciplinary Group of Marine Biotechnology (GIBMAR), Center for Biotechnology, University of Concepcion, Concepcion, Chile.,Interdisciplinary Research Laboratory in Mycotoxins, Department of Food Science and Technology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile
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24
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Liu W, Zeng S, Wu G, Li H, Chen F. Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model. SENSORS (BASEL, SWITZERLAND) 2021; 21:4384. [PMID: 34206783 PMCID: PMC8271842 DOI: 10.3390/s21134384] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 11/25/2022]
Abstract
Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67-100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60-100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.
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Affiliation(s)
- Weihua Liu
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China;
| | - Shan Zeng
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
| | - Guiju Wu
- The Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430023, China;
| | - Hao Li
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
| | - Feifei Chen
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
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25
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Niu X, Zhang J, Wang C, Jia X, Fu J, Suo Y. Evaluation of the lignite biotransformation capacity of Fusarium sp. NF01 cultured on different growth substrates. Can J Microbiol 2021; 67:613-621. [PMID: 33751915 DOI: 10.1139/cjm-2020-0157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The screening and studying the lignite solubilization/degradation capacities of indigenous microorganisms are key to exploring the in-situ biotransformation of lignite. Herein, a fungus was isolated from in-situ lignite samples and identified as Fusarium sp. NF01. This isolate was then cultured on four different carbon sources to evaluate its lignite-transformation capacity. When cultured on a solid agar medium containing sodium gluconate or sodium glutamate, Fusarium sp. NF01 completely liquefied 0.5 g of lignite within 6 days, and when cultured in a liquid medium containing sodium gluconate, the weight of lignite decreased by 28.4% within 7 days. Elemental analysis showed that the rate of lignite biodegradation was inversely proportional to the C:O ratio of the residual lignite samples. Additionally, a 5.9% biodesulfurization rate was achieved when Fusarium sp. NF01 was cultured in the presence of sodium gluconate. Finally, Fourier-transform infrared analysis of the residual lignite samples revealed relatively weak signal intensities of the signature peaks representing the following: aromatic ring side chains; ether, ester, and alcohol bonds; aromatic ring carbon-carbon double bonds; and aliphatic methyl and methylene. The results show that Fusarium sp. NF01 degrades lignite in a carbon-dependent manner and could be thus used for the bioconversion of subsurface coalbeds.
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Affiliation(s)
- Xian Niu
- School of Energy, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, P.R. China.,School of Mining and Technology, Inner Mongolia University of Technology, Hohhot 010051, P.R. China
| | - Jianbin Zhang
- College of Chemical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Cuiyan Wang
- College of Chemical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Xiaoqian Jia
- College of Chemical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Jilagamazhi Fu
- College of Chemical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Yonglu Suo
- School of Energy, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, P.R. China
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26
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27
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Femenias A, Bainotti MB, Gatius F, Ramos AJ, Marín S. Standardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level. Food Res Int 2020; 139:109925. [PMID: 33509492 DOI: 10.1016/j.foodres.2020.109925] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 10/22/2022]
Abstract
The spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R2 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100% of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4% of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.
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Affiliation(s)
- Antoni Femenias
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Maria Belén Bainotti
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Ferran Gatius
- Departament de Química, Universitat de Lleida (UdL), Av. Rovira Roure, 191, Lleida 25198, Spain
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, University of Lleida, UTPV-XaRTA, Agrotecnio, Av. Rovira Roure 191, 25198 Lleida, Spain.
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Caramês ETDS, Piacentini KC, Alves LT, Pallone JAL, Rocha LDO. NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2020; 37:1542-1552. [PMID: 32717175 DOI: 10.1080/19440049.2020.1778189] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Deoxynivalenol (DON) is one of the mycotoxins produced mainly by the Fusarium graminearum species complex in small grain cereals, including barley. This toxin can cause alimentary disorders, immune function depression and gastroenteritis. The negative health effects associated with DON coupled to the increasing concern about green and rapid methods of analysis motivated this study. In this context, near infrared (NIR) spectroscopy data were applied for exploratory analysis to distinguish barley with high and low levels of DON contamination (> or <1250 µg/kg according to the European Union threshold), by Partial Least Squares-Discriminant Analysis (PLS-DA), and to verify the performance of Partial Least Squares-Regression (PLS-R) to predict DON concentration in barley samples. Maximum values of specificity and sensitivity were achieved in the calibration set; 90.9% and 81.9% were observed in the cross-validation set for the PLS-DA classification model. PLS-R quantification of DON in barley presented low values of error (RMSEC = 101.94 µg/kg and RMSEP = 160.76 µg/kg). Thus, we found that NIR in combination with adequate chemometric tools could be applied as a green technique to monitor DON contamination in barley.
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Affiliation(s)
| | - Karim C Piacentini
- Department of Food Science, School of Food Engineering, State University of Campinas , Campinas, São Paulo, Brazil
| | - Lucas Teixeira Alves
- Department of Food Science, School of Food Engineering, State University of Campinas , Campinas, São Paulo, Brazil
| | - Juliana Azevedo Lima Pallone
- Department of Food Science, School of Food Engineering, State University of Campinas , Campinas, São Paulo, Brazil
| | - Liliana de Oliveira Rocha
- Department of Food Science, School of Food Engineering, State University of Campinas , Campinas, São Paulo, Brazil
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Vila-Donat P, Marín S, Sanchis V, Ramos AJ. Tri-octahedral bentonites as potential technological feed additive for Fusarium mycotoxin reduction. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2020; 37:1374-1387. [PMID: 32529969 DOI: 10.1080/19440049.2020.1766702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In 2009 the EU Regulation 386/2009 established a new functional group of feed additives called "substances for reduction of the contamination of feed by mycotoxin". Later, di-octahedral bentonite (1 m558) was authorised, as an anti-aflatoxin additive, being the only additive of this group authorised to date. This work aims to demonstrate the effectiveness of other bentonites, such as tri-octahedral bentonites, versus Fusarium-mycotoxins, since very few adsorbents have proved their effectiveness in relation to this group of mycotoxins. For this purpose, 7 bentonites (six of them tri-octahedral) and 7 commercial adsorbents, added at 0.02% (w/v), were assayed in an in vitro adsorption experiment using two simulated gastro-intestinal (GI) juices (pH 1.3 and 6.8) versus zearalenone (ZEN: 0.1-5 mg/L), fumonisin B1 (FB1: 1-10 mg/L) and deoxynivalenol (DON: 2-10 mg/L). Mycotoxin adsorption data were fitted to Langmuir and Freundlich isotherms. In vitro adsorption experiments showed that ZEN and FB1 (in the latter case only in acid medium) were partially adsorbed, while the adsorption of DON was negligible. Moreover, the increase of adsorbent dose (up to 0.20%, w/v) significantly improved the in vitro adsorption of ZEN and FB1, reaching >90% of adsorption. The present work proposes the use of some tri-octahedral bentonites as feed additives for Fusarium-mycotoxin reduction.
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Affiliation(s)
- Pilar Vila-Donat
- Applied Mycology Unit, Food Technology Department, UTPV-XaRTA, Agrotecnio, University of Lleida , Lleida, Spain
| | - Sonia Marín
- Applied Mycology Unit, Food Technology Department, UTPV-XaRTA, Agrotecnio, University of Lleida , Lleida, Spain
| | - Vicente Sanchis
- Applied Mycology Unit, Food Technology Department, UTPV-XaRTA, Agrotecnio, University of Lleida , Lleida, Spain
| | - Antonio J Ramos
- Applied Mycology Unit, Food Technology Department, UTPV-XaRTA, Agrotecnio, University of Lleida , Lleida, Spain
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30
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Femenias A, Gatius F, Ramos AJ, Sanchis V, Marín S. Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.107074] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Agriopoulou S, Stamatelopoulou E, Varzakas T. Advances in Analysis and Detection of Major Mycotoxins in Foods. Foods 2020; 9:E518. [PMID: 32326063 PMCID: PMC7230321 DOI: 10.3390/foods9040518] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 04/16/2020] [Indexed: 12/19/2022] Open
Abstract
Mycotoxins are the most widely studied biological toxins, which contaminate foods at very low concentrations. This review describes the emerging extraction techniques and the current and alternatives analytical techniques and methods that have been used to successfully detect and identify important mycotoxins. Some of them have proven to be particularly effective in not only the detection of mycotoxins, but also in detecting mycotoxin-producing fungi. Chromatographic techniques such as high-performance liquid chromatography coupled with various detectors like fluorescence, diode array, UV, liquid chromatography coupled with mass spectrometry, and liquid chromatography-tandem mass spectrometry, have been powerful tools for analyzing and detecting major mycotoxins. Recent progress of the development of rapid immunoaffinity-based detection techniques such as immunoassays and biosensors, as well as emerging technologies like proteomic and genomic methods, molecular techniques, electronic nose, aggregation-induced emission dye, quantitative NMR and hyperspectral imaging for the detection of mycotoxins in foods, have also been presented.
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Affiliation(s)
| | | | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece; (S.A.); (E.S.)
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Beć KB, Grabska J, Bonn GK, Popp M, Huck CW. Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review. FRONTIERS IN PLANT SCIENCE 2020; 11:1226. [PMID: 32849759 PMCID: PMC7427587 DOI: 10.3389/fpls.2020.01226] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/27/2020] [Indexed: 05/08/2023]
Abstract
Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.
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Affiliation(s)
- Krzysztof B. Beć
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
| | - Justyna Grabska
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
| | - Günther K. Bonn
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- ADSI, Austrian Drug Screening Institute, Innsbruck, Austria
| | - Michael Popp
- Michael Popp Research Institute for New Phyto Entities, University of Innsbruck, Innsbruck, Austria
| | - Christian W. Huck
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
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