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Adesokan M, Alamu EO, Fawole S, Maziya-Dixon B. Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry. Front Chem 2023; 11:1156718. [PMID: 37234202 PMCID: PMC10206270 DOI: 10.3389/fchem.2023.1156718] [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: 02/01/2023] [Accepted: 05/02/2023] [Indexed: 05/27/2023] Open
Abstract
Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its preparation involves several unit operations, including fermentation, which is essential in gari production. Fermentation brings about specific biochemical changes in cassava starch due to the actions of lactic acid bacteria. Consequently, it gives rise to organic acids and a significant reduction in the pH. Consumer preferences for gari are influenced by these changes and impact specific functional characteristics, which are often linked to cassava genotypes. Measurement of these functional characteristics is time-consuming and expensive. Therefore, this study aimed to develop high-throughput and less expensive prediction models for water absorption capacity, swelling power, bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy (NIRS). Gari was produced from 63 cassava genotypes using the standard method developed in the RTB foods project. The prediction model was developed by dividing the gari samples into two sets of 48 samples for calibration and 15 samples as the validation set. The gari samples were transferred into a ring cell cup and scanned on the NIRS machine within the Vis-NIR range of 400-2,498 nm wavelength, though only the NIR range of 800-2,400 nm was used to build the model. Calibration models were developed using partial least regression algorithms after spectra pre-processing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. Results showed an excellent coefficient of determination in calibrations (R2 Cal) of 0.99, 0.97, 0.97, and 0.89 for bulk density, swelling power, dispersibility, and water absorption capacity, respectively. Also, the performances of the prediction models were tested using an independent set of 15 gari samples. A good prediction coefficient (R2 pred) and low standard error of prediction (SEP) was obtained as follows: Bulk density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and solubility index (0.62), respectively. Therefore, NIRS prediction models in this study could provide a rapid screening tool for cassava breeding programs and food scientists to determine the food quality of cassava granular products (Gari).
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Affiliation(s)
- Michael Adesokan
- Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Emmanuel Oladeji Alamu
- Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- International Institute of Tropical Agriculture, Southern Africa Research and Administration Hub (SARAH) Campus, Lusaka, Zambia
| | - Segun Fawole
- Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Busie Maziya-Dixon
- Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
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Herath S, Weerasooriya HK, Ranasinghe DYL, Bandara WGC, Herath VR, Godaliyadda RI, Ekanayake MPB, Madhujith T. Quantitative assessment of adulteration of coconut oil using transmittance multispectral imaging. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023; 60:1551-1559. [PMID: 37033321 PMCID: PMC10076459 DOI: 10.1007/s13197-023-05697-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 01/18/2023] [Accepted: 02/18/2023] [Indexed: 03/03/2023]
Abstract
Economical to a fault, coconut oil is a commodity related to fraudulent activities such as oil adulteration for undue profits. Unfortunately, the conventional methods used in the detection of adulteration and toxicants are laborious, destructive, and time-consuming. Hence, it is imperative to engineer a non-destructive and rapid screening test with sufficient accuracy. To that end, the proposed work has an in-house developed imaging system hardware and a method to estimate relevant quality parameters from multispectral imagery. Multispectral images of adulterated coconut oil were analyzed through a cascade of statistical algorithms: Fisher Discriminant Analysis and Bhattacharyya distance respectively. In this work, a functional relationship was developed for the estimation of adulteration level that recorded an R2 of 0.9876 for the training samples and an MSE of 0.0029 for the testing samples. Besides, the proposed imaging system offers flexibility on post-processing of raw measurements as the algorithm is designed to operate from raw multispectral images. In addition, the developed imaging system is economical in its capacity to estimate the adulteration of coconut oil with remarkable accuracy considering the low cost of production. Moreover, the proposed work validates the use of multispectral imagery as an initial screening technique instead of expensive spectroscopy methods.
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Affiliation(s)
- Sanjaya Herath
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, 20400 Sri Lanka
| | - Hashan Kavinga Weerasooriya
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, 20400 Sri Lanka
| | | | - Wele Gedara Chaminda Bandara
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, 20400 Sri Lanka
| | - Vijitha Rohana Herath
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, 20400 Sri Lanka
| | - Roshan Indika Godaliyadda
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, 20400 Sri Lanka
| | | | - Terrence Madhujith
- Department of Food Science and Technology, Faculty of Agriculture, University of Peradeniya, Peradeniya, 20400 Sri Lanka
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Wu Q, Mousa MA, Al-qurashi AD, Ibrahim OH, Abo-Elyousr KA, Rausch K, Abdel Aal AM, Kamruzzaman M. Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Curr Res Food Sci 2023; 6:100483. [PMID: 37033735 PMCID: PMC10073987 DOI: 10.1016/j.crfs.2023.100483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.
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Bala M, Sethi S, Sharma S, Mridula D, Kaur G. Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:1294-1302. [PMID: 36098480 DOI: 10.1002/jsfa.12223] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/08/2022] [Accepted: 09/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. RESULTS The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. CONCLUSION The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Manju Bala
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Swati Sethi
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Sanjula Sharma
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - D Mridula
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Gurpreet Kaur
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
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He HJ, Chen Y, Li G, Wang Y, Ou X, Guo J. Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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6
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Martínez-Martín I, Hernández-Jiménez M, Revilla I, Vivar-Quintana AM. Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:1491. [PMID: 36772530 PMCID: PMC9920201 DOI: 10.3390/s23031491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.
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Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review. Foods 2023; 12:foods12010199. [PMID: 36613416 PMCID: PMC9818512 DOI: 10.3390/foods12010199] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/26/2022] [Accepted: 12/28/2022] [Indexed: 01/04/2023] Open
Abstract
Food adulteration refers to the alteration of food quality that takes place deliberately. It includes the addition of ingredients to modify different properties of food products for economic advantage. Color, appearance, taste, weight, volume, and shelf life are such food properties. Substitution of food or its nutritional content is also accomplished to spark the apparent quality. Substitution with species, protein content, fat content, or plant ingredients are major forms of food substitution. Origin misrepresentation of food is often practiced to increase the market demand of food. Organic and synthetic compounds are added to ensure a rapid effect on the human body. Adulterated food products are responsible for mild to severe health impacts as well as financial damage. Diarrhea, nausea, allergic reaction, diabetes, cardiovascular disease, etc., are frequently observed illnesses upon consumption of adulterated food. Some adulterants have shown carcinogenic, clastogenic, and genotoxic properties. This review article discusses different forms of food adulteration. The health impacts also have been documented in brief.
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8
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Rady A, Watson N. Detection and quantification of peanut contamination in garlic powder using NIR sensors and machine learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Near-infrared spectroscopy and machine learning for classification of food powders under moving conditions. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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10
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Wang Z, Wu Q, Kamruzzaman M. Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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11
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Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2022; 59:3130-3138. [PMID: 35505664 PMCID: PMC9051818 DOI: 10.1007/s13197-022-05456-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 02/08/2022] [Accepted: 03/25/2022] [Indexed: 11/17/2022]
Abstract
The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.
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12
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Determination of Total Volatile Basic Nitrogen (TVB-N) Content in Beef Based on Airflow and Multipoint Laser Technique. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02360-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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13
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Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage. Foods 2022; 11:foods11142024. [PMID: 35885270 PMCID: PMC9322043 DOI: 10.3390/foods11142024] [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: 05/01/2022] [Revised: 06/27/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023] Open
Abstract
S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.
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14
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Monitoring of moisture contents and rehydration rates of microwave vacuum and hot air dehydrated beef slices and splits using hyperspectral imaging. Food Chem 2022; 382:132346. [DOI: 10.1016/j.foodchem.2022.132346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 01/05/2022] [Accepted: 02/01/2022] [Indexed: 01/17/2023]
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15
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Yao K, Sun J, Chen C, Xu M, Zhou X, Cao Y, Tian Y. Non-destructive detection of egg qualities based on hyperspectral imaging. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111024] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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16
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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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17
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Zheng L, Bao Q, Weng S, Tao J, Zhang D, Huang L, Zhao J. Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120813. [PMID: 34998050 DOI: 10.1016/j.saa.2021.120813] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.
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Affiliation(s)
- Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.
| | - Qian Bao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jianpeng Tao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
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18
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Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese Rice Wine) by near infrared spectroscopy combined with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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19
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Pandiselvam R, Mahanti NK, Manikantan M, Kothakota A, Chakraborty SK, Ramesh S, Beegum PS. Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108588] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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20
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Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Curr Res Food Sci 2022; 5:1305-1312. [PMID: 36065198 PMCID: PMC9440252 DOI: 10.1016/j.crfs.2022.08.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022] Open
Abstract
The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products. NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature. Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis. Online and handheld NIR spectrometers for wheat flour detection are the future trends.
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Gopalakrishnan K, Sharma A, Emanuel N, Prabhakar PK, Kumar R. Sensors for Non‐Destructive Quality Evaluation of Food. Food Chem 2021. [DOI: 10.1002/9781119792130.ch13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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22
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Ghnimi H, Ennouri M, Chèné C, Karoui R. A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks. Crit Rev Food Sci Nutr 2021:1-24. [PMID: 34875937 DOI: 10.1080/10408398.2021.2012124] [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: 10/19/2022]
Abstract
The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.
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Affiliation(s)
- Hayet Ghnimi
- INRAE, Junia, Université d'Artois, University of Lille, Université du Littoral Côte d'Opale, Université de Picardie Jules Verne, Université de Liège, Lens, France.,Higher Institute of Biotechnology of Monastir, University of Monastir, Monastir, Tunisia.,National Engineering School of Sfax, University of Sfax, LR11ES45, Sfax, Tunisia
| | - Monia Ennouri
- Olive Tree Institute, University of Sfax, LR16IO01, Sfax, Tunisia
| | - Christine Chèné
- Tilloy Les Mofflaines, Adrianor, Tilloy-lès-Mofflaines, France
| | - Romdhane Karoui
- INRAE, Junia, Université d'Artois, University of Lille, Université du Littoral Côte d'Opale, Université de Picardie Jules Verne, Université de Liège, Lens, France
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23
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Müller-Maatsch J, van Ruth SM. Handheld Devices for Food Authentication and Their Applications: A Review. Foods 2021; 10:2901. [PMID: 34945454 PMCID: PMC8700508 DOI: 10.3390/foods10122901] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/18/2021] [Accepted: 11/21/2021] [Indexed: 12/18/2022] Open
Abstract
This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.
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Affiliation(s)
- Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
| | - Saskia M. van Ruth
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
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24
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Su WH, Xue H. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods 2021; 10:2146. [PMID: 34574253 PMCID: PMC8472741 DOI: 10.3390/foods10092146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
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Affiliation(s)
- Wen-Hao Su
- Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing 100083, China;
| | - Huidan Xue
- School of Economics and Management, Beijing University of Technology, Beijing 100124, China
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25
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Ayvaz H, Korkmaz F, Polat H, Ayvaz Z, Barış Tuncel N. Detection of einkorn flour adulteration in flour and bread samples using Computer-Based Image Analysis and Near-Infrared Spectroscopy. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108162] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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26
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Keskin M, Arslan A, Soysal Y, Sekerli YE, Celiktas N. Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15846] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Muharrem Keskin
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Aysel Arslan
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Yurtsever Soysal
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Yunus Emre Sekerli
- Department of Biosystems Engineering Faculty of Agriculture Hatay Mustafa Kemal University Antakya Hatay Turkey
| | - Nafiz Celiktas
- Department of Field Crops Faculty of Agriculture Hatay Mustafa Kemal University Antakya, Hatay Turkey
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27
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Liu HY, Wadood SA, Xia Y, Liu Y, Guo H, Guo BL, Gan RY. Wheat authentication:An overview on different techniques and chemometric methods. Crit Rev Food Sci Nutr 2021; 63:33-56. [PMID: 34196234 DOI: 10.1080/10408398.2021.1942783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.
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Affiliation(s)
- Hong-Yan Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Syed Abdul Wadood
- Department of Food and Nutrition, University of Home Economics, Lahore, Pakistan
| | - Yu Xia
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Yi Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Huan Guo
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Bo-Li Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ren-You Gan
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China.,Key Laboratory of Coarse Cereal Processing (Ministry of Agriculture and Rural Affairs), Sichuan Engineering & Technology Research Center of Coarse Cereal Industrialization, Chengdu University, Chengdu, China
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28
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Dimitrakopoulou ME, Vantarakis A. Does Traceability Lead to Food Authentication? A Systematic Review from A European Perspective. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1923028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
| | - Apostolos Vantarakis
- Department of Public Health, Medical School, University of Patras, Patras, Greece
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29
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Alamu EO, Nuwamanya E, Cornet D, Meghar K, Adesokan M, Tran T, Belalcazar J, Desfontaines L, Davrieux F. Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review. Int J Food Sci Technol 2021; 56:1491-1501. [PMID: 33776247 PMCID: PMC7984172 DOI: 10.1111/ijfs.14773] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 01/20/2023]
Abstract
The review aimed to identify the different high‐throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high‐throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid‐infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.
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Affiliation(s)
- Emmanuel Oladeji Alamu
- International Institute of Tropical Agriculture (IITA) Southern Africa Hub PO Box 310142 Chelstone, Lusaka Zambia.,International Institute of Tropical Agriculture (IITA) PMB 5320, Oyo Road Ibadan Oyo State Nigeria
| | - Ephraim Nuwamanya
- National Crops Resources Research Institute NaCRRI P.O Box 7084 Kampala Uganda
| | - Denis Cornet
- CIRAD UMR AGAP Montpellier F-34398 France.,Univ. Montpellier CIRAD INRA Montpellier SupAgro France
| | - Karima Meghar
- UMR Qualisud University of Montpellier CIRAD Montpellier SupAgro University of Avignon University of La Réunion 73 rue JF Breton Montpellier 34398 France
| | - Michael Adesokan
- International Institute of Tropical Agriculture (IITA) PMB 5320, Oyo Road Ibadan Oyo State Nigeria
| | - Thierry Tran
- UMR Qualisud University of Montpellier CIRAD Montpellier SupAgro University of Avignon University of La Réunion 73 rue JF Breton Montpellier 34398 France.,The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) CGIAR Research Program on Roots Tubers and Bananas (RTB) Apartado Aéreo 6713 Cali Colombia
| | - John Belalcazar
- The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) CGIAR Research Program on Roots Tubers and Bananas (RTB) Apartado Aéreo 6713 Cali Colombia
| | - Lucienne Desfontaines
- Centre de recherche Antilles-Guyane INRAe UR 1321 ASTRO Agrosystèmes tropicaux Petit-Bourg France
| | - Fabrice Davrieux
- UMR Qualisud University of Montpellier CIRAD Montpellier SupAgro University of Avignon University of La Réunion 73 rue JF Breton Montpellier 34398 France
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30
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Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. REMOTE SENSING 2020. [DOI: 10.3390/rs13010026] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.
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31
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Su WH, Yang C, Dong Y, Johnson R, Page R, Szinyei T, Hirsch CD, Steffenson BJ. Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. Food Chem 2020; 343:128507. [PMID: 33160773 DOI: 10.1016/j.foodchem.2020.128507] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 10/23/2022]
Abstract
Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-RCV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.
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Affiliation(s)
- Wen-Hao Su
- Department of Agricultural Engineering, College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China.
| | - Ce Yang
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA.
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Ryan Johnson
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Rae Page
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Tamas Szinyei
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Cory D Hirsch
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Brian J Steffenson
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
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32
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Faqeerzada MA, Lohumi S, Kim G, Joshi R, Lee H, Kim MS, Cho BK. Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5855. [PMID: 33081195 PMCID: PMC7589775 DOI: 10.3390/s20205855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/17/2020] [Accepted: 10/11/2020] [Indexed: 11/16/2022]
Abstract
The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.
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Affiliation(s)
- Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Santosh Lohumi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Geonwoo Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, BARC-East, Beltsville, MD 20705, USA;
| | - Rahul Joshi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea;
| | - Moon Sung Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, BARC-East, Beltsville, MD 20705, USA;
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
- Department of Smart Agriculture System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
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33
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Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review. SMART CITIES 2020. [DOI: 10.3390/smartcities3030039] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.
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34
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He Y, Bai X, Xiao Q, Liu F, Zhou L, Zhang C. Detection of adulteration in food based on nondestructive analysis techniques: a review. Crit Rev Food Sci Nutr 2020; 61:2351-2371. [PMID: 32543218 DOI: 10.1080/10408398.2020.1777526] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
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35
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A Review Towards Hyperspectral Imaging for Real-Time Quality Control of Food Products with an Illustrative Case Study of Milk Powder Production. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-020-02433-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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36
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He P, Wu Y, Wang J, Ren Y, Ahmad W, Liu R, Ouyang Q, Jiang H, Chen Q. Detection of mites
Tyrophagus putrescentiae
and
Cheyletus eruditus
in flour using hyperspectral imaging system coupled with chemometrics. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13386] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Peihuan He
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Yi Wu
- Institute of Grain Storage and Transport, Academy of National Food and Strategic Reserves Administration Beijing China
| | - Jingjing Wang
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Yi Ren
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
- School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture Suzhou China
| | - Waqas Ahmad
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Rui Liu
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Qin Ouyang
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
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37
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Ndlovu PF, Magwaza LS, Tesfay SZ, Mphahlele RR. Rapid visible-near infrared (Vis-NIR) spectroscopic detection and quantification of unripe banana flour adulteration with wheat flour. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2019; 56:5484-5491. [PMID: 31749496 PMCID: PMC6838284 DOI: 10.1007/s13197-019-04020-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 06/28/2019] [Accepted: 08/08/2019] [Indexed: 11/28/2022]
Abstract
Unripe banana flour is a premium nutritious product with a potential to curb degenerative diseases through resistant starch and gluten free traits, however, with scant techniques to monitor adulteration practices. The objective of the present study was to determine the efficacy of visible-near infrared spectroscopy (Vis-NIR) spectroscopy (Vis-NIRS) in the detection and quantification of unripe banana flour adulteration with wheat flour. To do this, simulated adulteration of a composite banana flour was performed with different levels of wheat flour, in intervals of 20 g kg-1, ranging from 0 to 800 g kg-1. Each level was acquired in duplicate giving a total of 82 samples. Vis-NIR spectral data was acquired using a portable F-750 spectrometer in the range 447-1005 nm. Spectral data was analysed chemometrically using principle components analysis and partial least squares regression (PLSR), with 41 samples used as a calibration set and 41 for validation. The first two principal components accounted for 95% of spectral data variation, revealing five distinct clusters related to 0 g kg-1, 20-200 g kg-1, 220-400 g kg-1, 420-600 g kg-1 and 620-800 g kg-1 adulterated samples. The best PLSR model to predict wheat flour adulteration degrees in unripe banana flour was obtained using 2nd derivative Savitzky-Golay (19-point smoothing, 2nd order polynomial), showing the highest Rc 2 (0.991); Rp 2 (0.993); RPD (12.021) and the lowest RMSEC (2.226 g kg-1) and RMSEP (1.993 g kg-1) values. The obtained Vis-NIRS PLSR models therefore demonstrated the technology novelty in monitoring unripe banana flour quality by the processing industries and in retail markets during product verification.
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Affiliation(s)
- Phindile Faith Ndlovu
- 1Discipline of Crop and Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3201 South Africa
| | - Lembe Samukelo Magwaza
- 1Discipline of Crop and Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3201 South Africa
| | - Samson Zeray Tesfay
- 1Discipline of Crop and Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3201 South Africa
| | - Rebogile Ramaesele Mphahlele
- Postharvest Laboratory, Agricultural Research Council, Tropical and Subtropical Crops, Private Bag X11208, Nelspruit, 1200 South Africa
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Shao Y, Xuan G, Hu Z, Wang Y. Detection of adulterants and authenticity discrimination for coarse grain flours using NIR hyperspectral imaging. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13265] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
- Nanjing Research Institute for Agricultural MechanizationMinistry of Agriculture Nanjing China
| | - Guantao Xuan
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
- College of Agriculture, Food and Natural ResourcesUniversity of Missouri Columbia Missouri
| | - Zhichao Hu
- Nanjing Research Institute for Agricultural MechanizationMinistry of Agriculture Nanjing China
| | - Yongxian Wang
- College of Mechanical and Electrical EngineeringShandong Agricultural University Tai'an China
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Lianou A, Mencattini A, Catini A, Di Natale C, Nychas GJE, Martinelli E, Panagou EZ. Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4071. [PMID: 31547154 PMCID: PMC6806099 DOI: 10.3390/s19194071] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/12/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022]
Abstract
The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2-6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2-5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.
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Affiliation(s)
- Alexandra Lianou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome for Vergata, via del Politecnico 1, 00133 Roma, Italy.
| | - Alexandro Catini
- Department of Electronic Engineering, University of Rome for Vergata, via del Politecnico 1, 00133 Roma, Italy.
| | - Corrado Di Natale
- Department of Electronic Engineering, University of Rome for Vergata, via del Politecnico 1, 00133 Roma, Italy.
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome for Vergata, via del Politecnico 1, 00133 Roma, Italy.
| | - Efstathios Z Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
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40
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Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.07.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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41
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Identification of fiber added to semolina by near infrared (NIR) spectral techniques. Food Chem 2019; 289:195-203. [DOI: 10.1016/j.foodchem.2019.03.057] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 03/07/2019] [Accepted: 03/11/2019] [Indexed: 01/11/2023]
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42
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Su WH, Sun DW. Mid-infrared (MIR) Spectroscopy for Quality Analysis of Liquid Foods. FOOD ENGINEERING REVIEWS 2019. [DOI: 10.1007/s12393-019-09191-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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43
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Jun S, Xin Z, Xiaohong W, Bing L, Chunxia D, Jifeng S. Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 212:215-221. [PMID: 30641361 DOI: 10.1016/j.saa.2018.12.051] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 12/04/2018] [Accepted: 12/28/2018] [Indexed: 06/09/2023]
Abstract
The reliability and validity of Vis-NIR hyperspectral imaging were investigated for the determination of heavy metal content in tomato leaves under different cadmium stress. Besides, a method involving wavelet transform and least square support vector machine regression (WT-LSSVR) is proposed to select the optimal wavelength and establish the detection model. The Vis-NIR hyperspectral images of 405 tomato leaf samples were obtained and the whole region of tomato leaf sample spectral data was collected and preprocessed. In addition, WT-LSSVR is used to select optimal wavelength and establish the detection model using db4 and db6 as wavelet basis function, respectively. Furthermore, the best prediction performances for detecting cadmium (Cd) content in tomato leaves was obtained by second derivative (2nd Der) pre-processing method, with Rc2 of 0.9437, RMSEC of 0.0988 mg/kg, Rp2 of 0.8937, RMSEP of 0.2331 mg/kg, Rcv2 of 0.9357, RMSECV of 0.1455 mg/kg, RPD of 3.081 and bias of 0.00863 using db6 (daubechies 6) as wavelet basis function with wavelet fourth layer decomposition. The results of this study indicated that WT-LSSVR can effectively select the optimal wavelength and Vis-NIR hyperspectral imaging has great potential for detecting heavy metal content in tomato leaves under different cadmium stresses.
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Affiliation(s)
- Sun Jun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Zhou Xin
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Wu Xiaohong
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Lu Bing
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Dai Chunxia
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Shen Jifeng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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45
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Cost-sensitive stacked sparse auto-encoder models to detect striped stem borer infestation on rice based on hyperspectral imaging. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.01.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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46
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Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection. SENSORS 2019; 19:s19040846. [PMID: 30781709 PMCID: PMC6413199 DOI: 10.3390/s19040846] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/19/2019] [Accepted: 01/19/2019] [Indexed: 01/15/2023]
Abstract
The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.
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47
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Su WH, Bakalis S, Sun DW. Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00037-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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48
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Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods. Food Chem 2019; 274:392-401. [DOI: 10.1016/j.foodchem.2018.08.140] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 07/30/2018] [Accepted: 08/31/2018] [Indexed: 12/31/2022]
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49
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Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
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Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
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50
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Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8071076] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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