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Banah H, Balint-Kurti PJ, Houdinet G, Hawkes CV, Kudenov M. The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques. PLoS One 2024; 19:e0301779. [PMID: 38748689 PMCID: PMC11095743 DOI: 10.1371/journal.pone.0301779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/21/2024] [Indexed: 05/19/2024] Open
Abstract
Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.
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Affiliation(s)
- Hashem Banah
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, United States of America
- NC Plant Science Initiative, North Carolina State University, Raleigh, NC, United States of America
| | - Peter J. Balint-Kurti
- USDA-ARS, Plant Science Research Unit and Entomology and Plant Pathology Department, North Carolina State University, Raleigh, NC, United States of America
- NC Plant Science Initiative, North Carolina State University, Raleigh, NC, United States of America
| | - Gabriella Houdinet
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States of America
| | - Christine V. Hawkes
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States of America
| | - Michael Kudenov
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, United States of America
- NC Plant Science Initiative, North Carolina State University, Raleigh, NC, United States of America
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Mei J, Zhao F, Xu R, Huang Y. A review on the application of spectroscopy to the condiments detection: from safety to authenticity. Crit Rev Food Sci Nutr 2021; 62:6374-6389. [PMID: 33739226 DOI: 10.1080/10408398.2021.1901257] [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: 10/21/2022]
Abstract
Condiments are the magical ingredients that make the food present a richer taste. In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food condiments safety, authentication, and traceability. The potential of spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), Raman, fluorescence, inductively coupled plasma (ICP), and hyperspectral imaging techniques, has been widely enhanced by numerous applications in this field because of their advantages over other analytical techniques. Following a brief introduction of condiment and safety basics, this review mainly focuses on recent vibrational and atomic spectral applications for condiment nondestructive analysis and evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; and (3) authenticity concerns. The review shows current spectroscopies to be effective tools that will play indispensable roles for food condiment evaluation. In addition, online/real-time applications of these techniques promise to be a huge growth field in the near future.
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Affiliation(s)
- Jianhua Mei
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
| | - Fangyuan Zhao
- College of Food Science and Engineering, Qingdao Agricultural University, Qingdao, Shandong, 266109, P. R. China
| | - Runqi Xu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
| | - Yue Huang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P. R. China.,Health Food Industry Research Institute (Xinghua), China Agricultural University, Xinghua, Jiangsu, 225700, P. R. China
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New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence. Talanta 2020; 219:121238. [PMID: 32887129 DOI: 10.1016/j.talanta.2020.121238] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 01/06/2023]
Abstract
This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.
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Detection of Aflatoxins B1 in Maize Grains Using Fluorescence Resonance Energy Transfer. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051578] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Aflatoxins are secondary metabolites of Aspergillus flavus and Aspergillus parasiticus. These fungal species are the most dangerous and common toxin group causing food contamination. Aflatoxin has high toxicity and can cause cancer to humans and animals. The quantitative detection of aflatoxin in food, therefore, plays a very important role. However, in practice, due to low concentrations, aflatoxin detection analysis methods need to be highly sensitive and simple to apply. In this report, the fluorescence resonance energy transfer method (FRET) adopts the donor–acceptor interaction of aflatoxin B1. The CdSe/ZnS quantum dot detection of aflatoxin B1 will be presented wherein the aflatoxin B1 concentration can be determined from the changes in fluorescence lifetime or fluorescence intensity. A fluorescence lifetime calibration curve versus aflatoxin B1 concentrations was established. Test results of aflatoxin B1 determination in maize in Vietnam by FRET method are consistent with the results of aflatoxin B1 determination by HPLC based on ppm concentration.
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Shibata M, Chen J, Okada K, Hagiwara T. Detection of Food Residues on Stainless Steel Surfaces Using Fluorescence Fingerprint. FOOD SCIENCE AND TECHNOLOGY RESEARCH 2020. [DOI: 10.3136/fstr.26.389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Mario Shibata
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology
| | - Jizhong Chen
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology
| | - Kai Okada
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology
| | - Tomoaki Hagiwara
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology
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Wu Q, Xie L, Xu H. Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques. Food Chem 2018; 252:228-242. [DOI: 10.1016/j.foodchem.2018.01.076] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/06/2017] [Accepted: 01/09/2018] [Indexed: 12/29/2022]
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Discrimination of aflatoxin contamination level in nutmeg by fluorescence fingerprint measurement. Food Control 2018. [DOI: 10.1016/j.foodcont.2017.09.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Tao F, Yao H, Hruska Z, Burger LW, Rajasekaran K, Bhatnagar D. Recent development of optical methods in rapid and non-destructive detection of aflatoxin and fungal contamination in agricultural products. Trends Analyt Chem 2018. [DOI: 10.1016/j.trac.2017.12.017] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Analytical techniques combined with chemometrics for authentication and determination of contaminants in condiments: A review. J Food Compost Anal 2015. [DOI: 10.1016/j.jfca.2015.05.004] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Chen WT, Kuo YF. Measurement of Residual Bran Distribution on Milled Rice Using Fluorescence Fingerprint-derived Imaging. FOOD SCIENCE AND TECHNOLOGY RESEARCH 2015. [DOI: 10.3136/fstr.21.187] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Wei-Tung Chen
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University
| | - Yan-Fu Kuo
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University
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