1
|
Liu F, Yang R, Chen R, Lamine Guindo M, He Y, Zhou J, Lu X, Chen M, Yang Y, Kong W. Digital techniques and trends for seed phenotyping using optical sensors. J Adv Res 2024; 63:1-16. [PMID: 37956859 PMCID: PMC11380022 DOI: 10.1016/j.jare.2023.11.010] [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: 11/09/2022] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
Collapse
Affiliation(s)
- Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jun Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
| | - Xiangyu Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mengyuan Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yinhui Yang
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
| |
Collapse
|
2
|
Naeem I, Ismail A, Riaz M, Aziz M, Akram K, Shahzad MA, Ameen M, Ali S, Oliveira CAF. Aflatoxins in the rice production chain: A review on prevalence, detection, and decontamination strategies. Food Res Int 2024; 188:114441. [PMID: 38823858 DOI: 10.1016/j.foodres.2024.114441] [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: 01/24/2024] [Revised: 04/01/2024] [Accepted: 04/27/2024] [Indexed: 06/03/2024]
Abstract
Rice (Oryza sativa L.) is one of the most consumed cereals that along with several important nutritional constituents typically provide more than 21% of the caloric requirements of human beings. Aflatoxins (AFs) are toxic secondary metabolites of several Aspergillus species that are prevalent in cereals, including rice. This review provides a comprehensive overview on production factors, prevalence, regulations, detection methods, and decontamination strategies for AFs in the rice production chain. The prevalence of AFs in rice is more prominent in African and Asian than in European countries. Developed nations have more stringent regulations for AFs in rice than in the developing world. The contamination level of AFs in the rice varied at different stages of rice production chain and is affected by production practices, environmental conditions comprising temperature, humidity, moisture, and water activity as well as milling operations such as de-husking, parboiling, and polishing. A range of methods including chromatographic techniques, immunochemical methods, and spectrophotometric methods have been developed, and used for monitoring AFs in rice. Chromatographic methods are the most used methods of AFs detection followed by immunochemical techniques. AFs decontamination strategies adopted worldwide involve various physical, chemical, and biological strategies, and even using plant materials. In conclusion, adopting good agricultural practices, implementing efficient AFs detection methods, and developing innovative aflatoxin decontamination strategies are imperative to ensure the safety and quality of rice for consumers.
Collapse
Affiliation(s)
- Iqra Naeem
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Amir Ismail
- Department of Food Safety and Quality Management, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan.
| | - Muhammad Riaz
- Department of Food Safety and Quality Management, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Mubashir Aziz
- Department of Microbiology and Molecular Genetics, Bahauddin Zakariya University, Multan, Pakistan
| | - Kashif Akram
- Department of Food Science, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan
| | - Muhammad A Shahzad
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Mavra Ameen
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Sher Ali
- Department of Food Engineering, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Carlos A F Oliveira
- Department of Food Engineering, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil.
| |
Collapse
|
3
|
Logan N, Cao C, Freitag S, Haughey SA, Krska R, Elliott CT. Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS). ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309625. [PMID: 38224595 DOI: 10.1002/adma.202309625] [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: 09/18/2023] [Revised: 12/20/2023] [Indexed: 01/17/2024]
Abstract
The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.
Collapse
Affiliation(s)
- Natasha Logan
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
| | - Cuong Cao
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
- Material and Advanced Technologies for Healthcare, Queen's University Belfast, 18-30 Malone Road, Belfast, BT9 5BN, UK
| | - Stephan Freitag
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Konrad-Lorenz-Str. 20, Tulln, 3430, Vienna, Austria
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, 3430, Austria
| | - Simon A Haughey
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
| | - Rudolf Krska
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
- Department of Agrobiotechnology IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Konrad-Lorenz-Str. 20, Tulln, 3430, Vienna, Austria
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Technopark 1C, Tulln, 3430, Austria
| | - Christopher T Elliott
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Khong Luang, Pathum Thani, 12120, Thailand
| |
Collapse
|
4
|
Yao S, Miyagusuku-Cruzado G, West M, Nwosu V, Dowd E, Fountain J, Giusti MM, Rodriguez-Saona LE. Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman). Foods 2024; 13:157. [PMID: 38201185 PMCID: PMC10779085 DOI: 10.3390/foods13010157] [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: 11/30/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.
Collapse
Affiliation(s)
- Siyu Yao
- Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210009, China
| | - Gonzalo Miyagusuku-Cruzado
- Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA (M.M.G.); (L.E.R.-S.)
| | - Megan West
- Mars Wrigley, Inc., 1132 W. Blackhawk Street, Chicago, IL 60642, USA (E.D.)
| | - Victor Nwosu
- Mars Wrigley, Inc., 1132 W. Blackhawk Street, Chicago, IL 60642, USA (E.D.)
| | - Eric Dowd
- Mars Wrigley, Inc., 1132 W. Blackhawk Street, Chicago, IL 60642, USA (E.D.)
| | - Jake Fountain
- Department of Plant Pathology, University of Georgia, 216 Redding Building, 1109 Experiment St., Griffin, GA 30223, USA
| | - M. Monica Giusti
- Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA (M.M.G.); (L.E.R.-S.)
| | - Luis E. Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA (M.M.G.); (L.E.R.-S.)
| |
Collapse
|
5
|
Rapid Biomarker-Based Diagnosis of Fibromyalgia Syndrome and Related Rheumatologic Disorders by Portable FT-IR Spectroscopic Techniques. Biomedicines 2023; 11:biomedicines11030712. [PMID: 36979691 PMCID: PMC10044908 DOI: 10.3390/biomedicines11030712] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 03/02/2023] Open
Abstract
Fibromyalgia syndrome (FM), one of the most common illnesses that cause chronic widespread pain, continues to present significant diagnostic challenges. The objective of this study was to develop a rapid vibrational biomarker-based method for diagnosing fibromyalgia syndrome and related rheumatologic disorders (systemic lupus erythematosus (SLE), osteoarthritis (OA) and rheumatoid arthritis (RA)) through portable FT-IR techniques. Bloodspot samples were collected from patients diagnosed with FM (n = 122) and related rheumatologic disorders (n = 70), including SLE (n = 17), RA (n = 43), and OA (n = 10), and stored in conventional protein saver bloodspot cards. The blood samples were prepared by four different methods (blood aliquots, protein-precipitated extraction, and non-washed and water-washed semi-permeable membrane filtration extractions), and spectral data were collected with a portable FT-IR spectrometer. Pattern recognition analysis, OPLS-DA, was able to identify the signature profile and classify the spectra into corresponding classes (Rcv > 0.93) with excellent sensitivity and specificity. Peptide backbones and aromatic amino acids were predominant for the differentiation and might serve as candidate biomarkers for syndromes such as FM. This research evaluated the feasibility of portable FT-IR combined with chemometrics as an accurate and high-throughput tool for distinct spectral signatures of biomarkers related to the human syndrome (FM), which could allow for real-time and in-clinic diagnostics of FM.
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Yao W, Liu R, Zhang F, Li S, Huang X, Guo H, Peng M, Zhong G. Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy. Molecules 2022; 27:molecules27196294. [PMID: 36234831 PMCID: PMC9571819 DOI: 10.3390/molecules27196294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Aflatioxin B1 (AFB1) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB1-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB1 or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB1 in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (Rc2 = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB1 infection in the early stages.
Collapse
Affiliation(s)
- Wanqing Yao
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
| | - Ruanshan Liu
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Fengru Zhang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Shuang Li
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoxia Huang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Hongwei Guo
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Mengxia Peng
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Guohua Zhong
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
| |
Collapse
|
8
|
Green and sustainable technologies for the decontamination of fungi and mycotoxins in rice: A review. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
9
|
A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy. Toxins (Basel) 2022; 14:toxins14050323. [PMID: 35622570 PMCID: PMC9146547 DOI: 10.3390/toxins14050323] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 12/30/2022] Open
Abstract
Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.
Collapse
|
10
|
Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food. Foods 2022; 11:foods11070930. [PMID: 35407017 PMCID: PMC8997473 DOI: 10.3390/foods11070930] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.
Collapse
|
11
|
Jin B, Zhang C, Jia L, Tang Q, Gao L, Zhao G, Qi H. Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning. ACS OMEGA 2022; 7:4735-4749. [PMID: 35187294 PMCID: PMC8851633 DOI: 10.1021/acsomega.1c04102] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 01/20/2022] [Indexed: 05/15/2023]
Abstract
Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300-1400 nm and secondarily concentrated at approximately 1050-1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.
Collapse
Affiliation(s)
- Baichuan Jin
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Chu Zhang
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Liangquan Jia
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Qizhe Tang
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lu Gao
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Guangwu Zhao
- College
of Agriculture and Food Science, Zhejiang
Agriculture and Forestry University, Lin’an 311300, China
| | - Hengnian Qi
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| |
Collapse
|
12
|
Ong P, Tung IC, Chiu CF, Tsai IL, Shih HC, Chen S, Chuang YK. Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
13
|
Recent Progress in Near-Infrared Organic Electroluminescent Materials. Top Curr Chem (Cham) 2021; 380:6. [PMID: 34878603 DOI: 10.1007/s41061-021-00357-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/21/2021] [Indexed: 10/19/2022]
Abstract
Near-infrared (NIR) refers to the section of the spectrum from 650 to 2500 nm. NIR luminescent materials are widely employed in organic light-emitting diodes (OLEDs), fiber optic communication, sensing, biological detection, and medical imaging. This paper reviews organic NIR electroluminescent materials, including organic NIR electrofluorescent materials and organic NIR electrophosphorescent materials that have been investigated in the past 6 years. Small-molecule, polymer NIR fluorescent materials and platinum(II) and iridium(III) complex NIR phosphorescent materials are described, and the limitations of the development of NIR luminescent materials and future prospects are discussed.
Collapse
|
14
|
Jiang H, Wang J, Chen Q. Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106642] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
15
|
Gibellato S, Dalsóquio L, do Nascimento I, Alvarez T. Current and promising strategies to prevent and reduce aflatoxin contamination in grains and food matrices. WORLD MYCOTOXIN J 2021. [DOI: 10.3920/wmj2020.2559] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Mycotoxins are secondary metabolites produced by filamentous fungi that colonise various crops around the world and cause major damage to the agro-industrial sector on a global scale. Considering the estimative of population growth in the next decades, it is of fundamental importance the implementation of practices that help prevent the economics and social impacts of aflatoxin contamination. Even though various approaches have been developed – including physical, chemical and biological approaches – there is not yet one that strikes a balance in terms of safety, food quality and cost, especially when considering large scale application. In this review, we present a compilation of advantages and disadvantages of different strategies for prevention and reduction of aflatoxin contamination. Biological approaches represent the trend in innovations mainly due to their specificity and versatility, since it is possible to consider the utilisation of whole microorganisms, culture supernatants, purified enzymes or even genetic engineering. However, challenges related to improvement of the efficiency of such methods and ensuring safety of treated foods still need to be overcome.
Collapse
Affiliation(s)
- S.L. Gibellato
- Graduate Programme in Industrial Biotechnology, Universidade Positivo, Curitiba, Paraná, 81280-330, Brazil
| | - L.F. Dalsóquio
- Bioprocesses and Biotechnology Engineering, Universidade Positivo, Curitiba, Paraná, 81280-330, Brazil
| | - I.C.A. do Nascimento
- Bioprocesses and Biotechnology Engineering, Universidade Positivo, Curitiba, Paraná, 81280-330, Brazil
| | - T.M. Alvarez
- Graduate Programme in Industrial Biotechnology, Universidade Positivo, Curitiba, Paraná, 81280-330, Brazil
- Bioprocesses and Biotechnology Engineering, Universidade Positivo, Curitiba, Paraná, 81280-330, Brazil
| |
Collapse
|
16
|
Miao X, Miao Y, Gong H, Tao S, Chen Z, Wang J, Chen Y, Chen Y. NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 257:119700. [PMID: 33872949 DOI: 10.1016/j.saa.2021.119700] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.
Collapse
Affiliation(s)
- Xuexue Miao
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Ying Miao
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
| | - Haoru Gong
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Shuhua Tao
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China.
| | - Zuwu Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Jiemin Wang
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Yingzi Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Yancheng Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| |
Collapse
|
17
|
Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Compr Rev Food Sci Food Saf 2021; 20:4612-4651. [PMID: 34338431 DOI: 10.1111/1541-4337.12801] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022]
Abstract
Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.
Collapse
Affiliation(s)
- Gayatri Mishra
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Brajesh Kumar Panda
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Wilmer Ariza Ramirez
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Hyewon Jung
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Chandra B Singh
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia.,Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
| | - Sang-Heon Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Ivan Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| |
Collapse
|
18
|
Song H, Li F, Guang P, Yang X, Pan H, Huang F. Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models. J Food Prot 2021; 84:1315-1320. [PMID: 33710323 DOI: 10.4315/jfp-20-447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
Collapse
Affiliation(s)
- Han Song
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Feng Li
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Peiwen Guang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Xinhao Yang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Huanyu Pan
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| |
Collapse
|
19
|
Pandiselvam R, Sruthi NU, Kumar A, Kothakota A, Thirumdas R, Ramesh S, Cozzolino D. Recent Applications of Vibrational Spectroscopic Techniques in the Grain Industry. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1904253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- R. Pandiselvam
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - N. U. Sruthi
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Ankit Kumar
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science & Technology, Telangana, India
| | - S.V. Ramesh
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), the University of Queensland, Brisbane, Australia
| |
Collapse
|
20
|
Discriminant analysis of pyrrolizidine alkaloid contamination in bee pollen based on near-infrared data from lab-stationary and portable spectrometers. Eur Food Res Technol 2020. [DOI: 10.1007/s00217-020-03590-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractBee pollen may be contaminated with pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs), which are mainly detected by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS), even though the use of fast near-infrared (NIR) spectroscopy is an ongoing alternative. Therefore, the main challenge of this study was to assess the feasibility of both a lab-stationary (Foss) and a portable (Polispec) NIR spectrometer in 60 dehydrated bee pollen samples. After an ANOVA-feature selection of the most informative NIR spectral data, canonical discriminant analysis (CDA) was performed to distinguish three quantitative PA/PANO classes (µg/kg): < LOQ (0.4), low; 0.4–400, moderate; > 400, high. According to the LC–MS/MS analysis, 77% of the samples were contaminated with PAs/PANOs and the sum content of the 17 target analytes was higher than 400 µg/kg in 28% of the samples. CDA was carried out on a pool of 18 (Foss) and 22 (Polispec) selected spectral variables and allowed accurate classification of samples from the low class as confirmed by the high values of Matthews correlation coefficient (≥ 0.91) for both NIR spectrometers. Leave-one-out cross-validation highlighted precise recognition of samples characterised by a high PA/PANO content with a low misclassification rate (0.02) as false negatives. The most informative wavelengths were within the < 1000, 1000–1660 and > 2400 nm regions for Foss and > 1500 nm for Polispec that could be associated with cyclic amines, and epoxide chemical structures of PAs/PANOs. In sum, both lab-stationary and portable NIR systems are reliable and fast techniques for detecting PA/PANO contamination in bee pollen.
Collapse
|
21
|
Zheng SY, Wei ZS, Li S, Zhang SJ, Xie CF, Yao DS, Liu DL. Near-infrared reflectance spectroscopy-based fast versicolorin A detection in maize for early aflatoxin warning and safety sorting. Food Chem 2020; 332:127419. [PMID: 32622190 DOI: 10.1016/j.foodchem.2020.127419] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 05/29/2020] [Accepted: 06/23/2020] [Indexed: 10/24/2022]
Abstract
Aflatoxins (AFs) are potent carcinogens present in numerous crops. Access to accurate methods for evaluating contamination is a critical factor in aflatoxin risk assessment. Versicolorin A (Ver A), a precursor of aflatoxin B1 (AFB1), can be used as an indicator for the presence of AFB1, even when the AF is not yet detectable. Currently employed Ver A detection methods are expensive, time consuming, and difficult to apply to numerous samples. Herein, Ver A was detected via near-infrared spectroscopy. Both quantitative and two-grade sorting methods were set-up using the extreme gradient boosting algorithm coupled with a support vector machine. This two-tiered method obtained a root-mean-square error of prediction value of 3.57 μg/kg for the quantitative model, and an accuracy rate of 90.32% for the sorting approach. This novel method is rapid, accurate, solvent free, requires no sample pretreatment, and detects Ver A in maize, making it convenient for practical use.
Collapse
Affiliation(s)
- Shao-Yan Zheng
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China; National Engineering Research Center of Genetic Medicine, Jinan University, Guangzhou City, Guangdong Province 510632, China
| | - Ze-Shun Wei
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China
| | - Shuang Li
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China
| | - Shi-Jia Zhang
- Department of Bioengineering, Jinan University, Guangzhou City, Guangdong Province 510632, China
| | - Chun-Fang Xie
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China; Department of Bioengineering, Jinan University, Guangzhou City, Guangdong Province 510632, China; National Engineering Research Center of Genetic Medicine, Jinan University, Guangzhou City, Guangdong Province 510632, China
| | - Dong-Sheng Yao
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China; National Engineering Research Center of Genetic Medicine, Jinan University, Guangzhou City, Guangdong Province 510632, China.
| | - Da-Ling Liu
- Institute of Microbial Biotechnology, Jinan University, Guangzhou City, Guangdong Province 510632, China; Department of Bioengineering, Jinan University, Guangzhou City, Guangdong Province 510632, China.
| |
Collapse
|
22
|
Jia B, Wang W, Ni X, Chu X, Yoon S, Lawrence K. Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review. WORLD MYCOTOXIN J 2020. [DOI: 10.3920/wmj2019.2510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Nutrition-rich cereal grains and oil seeds are the major sources of food and feed for human and livestock, respectively. Infected by fungi and contaminated with mycotoxins are serious problems worldwide for cereals and oil seeds before and after harvest. The growth and development activities of fungi consume seed nutrients and destroy seed structures, leading to dramatic declines of crop yield and quality. In addition, the toxic secondary metabolites produced by these fungi pose a well-known threat to both human and animals. The existence of fungi and mycotoxins has been a redoubtable problem worldwide for decades but tends to be a severe food safety issue in developing countries and regions, such as China and Africa. Detection of fungal infection at an early stage and of mycotoxin contaminants, even at a small amount, is of great significance to prevent harmful toxins from entering the food supply chains worldwide. This review focuses on the recent advancements in utilising infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging to detect fungal infections and mycotoxin contaminants in cereals and oil seeds worldwide, with an emphasis on recent progress in China. Brief introduction of principles, and corresponding shortcomings, as well as latest advances of each technique, are also being presented herein.
Collapse
Affiliation(s)
- B. Jia
- Beijing Key Laboratory of Optimized Design for modern Agricultural Equipment, College of Engineering, China Agriculture University, No. 17 Tsinghua East Road, Beijing, 100083, China P.R
| | - W. Wang
- Beijing Key Laboratory of Optimized Design for modern Agricultural Equipment, College of Engineering, China Agriculture University, No. 17 Tsinghua East Road, Beijing, 100083, China P.R
| | - X.Z. Ni
- Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
| | - X. Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China P.R
| | - S.C. Yoon
- Quality and Safety Assessment Research Unit, USDA-ARS, Athens, GA 30605, USA
| | - K.C. Lawrence
- Quality and Safety Assessment Research Unit, USDA-ARS, Athens, GA 30605, USA
| |
Collapse
|
23
|
Li Z, Tang X, Shen Z, Yang K, Zhao L, Li Y. Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:5671-5679. [PMID: 31150109 DOI: 10.1002/jsfa.9828] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/17/2019] [Accepted: 05/27/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Aspergillus flavus is a major pollutant in moldy peanuts, and it has a large influence on the taste of food. The secondary metabolites of Aspergillus flavus, including aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2), are highly toxic and can expose humans to high risk. The total mold count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut. RESULTS Quantitative calibration models were established based on full-band wavelengths and characteristic wavelengths, combined with chemometric methods, to explore the feasibility of the use of near-infrared spectroscopy (NIRS) for rapid detection of the TMC in peanuts. The successive projection algorithm (SPA) and elimination of uninformative variables (UVE) algorithms were used to extract the characteristic wavelengths. In comparison, the model built by original spectrum, selected with the UVE algorithm, gave the best result, with a correlation coefficient in a prediction set (RP ) of 0.9577, a root mean square error for the prediction set (RMSEP) of 0.2336 Log CFU/g, and a residual predictive deviation (RPD) of 3.5041. CONCLUSIONS The results showed that NIRS is a rapid, practicable method for the quantitative detection of peanut Aspergillus flavus contamination. It is a promising method for detecting moldy peanuts and increasing peanut safety. © 2019 Society of Chemical Industry.
Collapse
Affiliation(s)
- Zhengxuan Li
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Xiuying Tang
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Zhixiong Shen
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Kefei Yang
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Lingjuan Zhao
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Yanlei Li
- College of Engineering, China Agricultural University, Beijing, PR China
| |
Collapse
|
24
|
Zhou W, Zhang J, Zou M, Liu X, Du X, Wang Q, Liu Y, Liu Y, Li J. Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:1848-1856. [PMID: 30456622 DOI: 10.1007/s11356-018-3745-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 11/12/2018] [Indexed: 06/09/2023]
Abstract
Cadmium (Cd) contaminated rice has become a global food security issue. Hyperspectral remote sensing can do rapid and nondestructive monitoring of environmental stress in plant. To realize the nondestructive detection of Cd in brown rice before harvest, the leaf spectral reflectance of rice exposed to six different levels of Cd stress was measured during the whole life stages. In addition, the dry weight of rice grain and Cd concentrations in brown rice were measured after harvest. The impact of Cd stress on the quantity and the quality of rice grain and on the leaf reflectance of rice was analyzed, and hyperspectral estimation models for predicting the Cd content in brown rice during three growth stages were established. The results showed that rice plants can impact the quality of the brown rice seriously, even if the impact on the quantity was not significant. All the established models had the capability to estimate Cd concentrations in brown rice (R2 > 0.598), and the best performance model, with the R2 value of 0.873, was use first derivative spectrum of booting stage as variable. It was concluded that the hyperspectral of rice leaves provides a new insight to predict Cd concentration in brown rice before harvest.
Collapse
Affiliation(s)
- Weihong Zhou
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
- Suzhou Institute of Technology, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
| | - Jingjing Zhang
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Mengmeng Zou
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Xiaoqing Liu
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Xiaolong Du
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Qian Wang
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Yangyang Liu
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Ying Liu
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China
| | - Jianlong Li
- School of Life Sciences, Nanjing University, Xianlin Road 163, Nanjing, 210000, People's Republic of China.
| |
Collapse
|
25
|
Wu Q, Xu J, Xu H. Interactions of aflatoxin B1 and related secondary metabolites with native cyclodextrins and their potential utilization. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.06.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|