1
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Song A, Wang C, Wen W, Zhao Y, Guo X, Zhao C. Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques. Food Chem 2024; 461:140932. [PMID: 39197321 DOI: 10.1016/j.foodchem.2024.140932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 08/08/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024]
Abstract
Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data. Initially, the study automatically screening the best model from up to 504 algorithm combinations. Subsequently, multi-stage grid search is utilized for improving precision. We collected 270 kernel samples from different parts of the ear from 15 high oil and regular corn materials, with oil contents ranging from 1.4% to 13.1%. Experimental results show that the combinations SG + NONE+KS + PLSR(R2: 0.8570) and MA + LAR+Random+MLR(R2: 0.8523) performed optimally. After parameter optimization, their R2 values increased to 0.9045 and 0.8730, respectively. Additionally, the ACNNR model achieved an R2 of 0.8878 and an RMSE of 0.2243. The improved algorithm significantly outperforms traditional methods and ACNNR model in prediction accuracy and adaptability, offering an effective method for field applications.
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Affiliation(s)
- Anran Song
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; Information Technology Research Center, Beijing, Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China
| | - Chuanyu Wang
- Information Technology Research Center, Beijing, Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China
| | - Weiliang Wen
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Yue Zhao
- Information Technology Research Center, Beijing, Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China
| | - Xinyu Guo
- Information Technology Research Center, Beijing, Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing, Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
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2
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Zou Z, Wang Q, Wu Q, Li M, Zhen J, Yuan D, Xiao Y, Xu C, Yin S, Zhou M, Xu L. Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity. Talanta 2024; 280:126793. [PMID: 39222596 DOI: 10.1016/j.talanta.2024.126793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/27/2024] [Accepted: 08/29/2024] [Indexed: 09/04/2024]
Abstract
Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the 'Convolutional Neural Network'-'Multilayer Perceptron' (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA-CNN-MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%-20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Menghua Li
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Dongyu Yuan
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Yuchen Xiao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Chong Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Shutao Yin
- Institute of Modern Agricultural Industry, China Agricultural University, Chengdu, Sichuan, 611430, China
| | - Man Zhou
- College of Food Science, Sichuan Agricultural University, Ya'an, 625014, China.
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China.
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3
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Aslam N, Fatima R, Altemimi AB, Ahmad T, Khalid S, Hassan SA, Aadil RM. Overview of industrial food fraud and authentication through chromatography technique and its impact on public health. Food Chem 2024; 460:140542. [PMID: 39079380 DOI: 10.1016/j.foodchem.2024.140542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/09/2024] [Accepted: 07/18/2024] [Indexed: 09/05/2024]
Abstract
Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.
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Affiliation(s)
- Nabila Aslam
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rida Fatima
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Ammar B Altemimi
- Food Science Department, College of Agriculture, University of Basrah, Basrah 61004, Iraq
| | - Talha Ahmad
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Samran Khalid
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Syed Ali Hassan
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
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4
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Mahanty S, Majumder S, Paul R, Boroujerdi R, Valsami-Jones E, Laforsch C. A review on nanomaterial-based SERS substrates for sustainable agriculture. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:174252. [PMID: 38942304 DOI: 10.1016/j.scitotenv.2024.174252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/06/2024] [Accepted: 06/22/2024] [Indexed: 06/30/2024]
Abstract
The agricultural sector plays a pivotal role in driving the economy of many developing countries. Any dent in this economical structure may have a severe impact on a country's population. With rising climate change and increasing pollution, the agricultural sector is experiencing significant damage. Over time this cumulative damage will affect the integrity of food crops and create food security issues around the world. Therefore, an early warning system is needed to detect possible stress on food crops. Here we present a review of the recent developments in nanomaterial-based Surface Enhanced Raman Spectroscopy (SERS) substrates which could be utilized to monitor agricultural crop responses to natural and anthropogenic stress. Initially, our review delves into diverse and cost-effective strategies for fabricating SERS substrates, emphasizing their intelligent utilization across various agricultural scenarios. In the second phase of our review, we spotlight the specific application of SERS in addressing critical food security issues. By detecting nutrients, hormones, and effector molecules in plants, SERS provides valuable insights into plant health. Furthermore, our exploration extends to the detection of contaminants, chemicals, and foodborne pathogens within plants, showcasing the versatility of SERS in ensuring food safety. The cumulative knowledge derived from these discussions illustrates the transformative potential of SERS in bolstering the agricultural economy. By enhancing precision in nutrient management, monitoring plant health, and enabling rapid detection of harmful substances, SERS emerges as a pivotal tool in promoting sustainable and secure agricultural practices. Its integration into agricultural processes not only augments productivity but also establishes a robust defence against potential threats to crop yield and food quality. As SERS continues to evolve, its role in shaping the future of agriculture becomes increasingly pronounced, promising a paradigm shift in how we approach and address challenges in food production and safety.
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Affiliation(s)
- Shouvik Mahanty
- Department of Atomic Energy, Saha Institute of Nuclear Physics, Sector 1, AF Block, Bidhannagar, Kolkata 700064, West Bengal, India
| | - Santanu Majumder
- Department of Life and Environmental Sciences, Bournemouth University (Talbot Campus), Fern Barrow, Poole BH12 5BB, UK.
| | - Richard Paul
- Department of Life and Environmental Sciences, Bournemouth University (Talbot Campus), Fern Barrow, Poole BH12 5BB, UK
| | - Ramin Boroujerdi
- Department of Life and Environmental Sciences, Bournemouth University (Talbot Campus), Fern Barrow, Poole BH12 5BB, UK
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Christian Laforsch
- Department of Animal Ecology I and BayCEER, University of Bayreuth, Bayreuth, Germany
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5
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Wang C, Sha T, Lu J, Guan Y, Geng X. A Miniaturized and Highly Sensitive "Windmill" Three-Channel Fluorescence Detector for Simultaneous Detection of Various Mycotoxins. Anal Chem 2024; 96:10121-10126. [PMID: 38874092 DOI: 10.1021/acs.analchem.4c00311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
A novel "windmill" three-channel light-emitting diode induced fluorescence detector (LED-IF) was proposed to maximize the excitation efficiency and fluorescence collection efficiency. Compared with the typical collinear arrangement, the fluorescence intensity of the three channels was increased by 7.85, 3.88, and 2.94 times, respectively. The compact shaping optical path was designed to obtain higher excitation efficiency and a lower background stray light effect caused by high divergence angle high-power ultraviolet (UV)-LEDs simultaneously, which increased the sensitivity of three channels by 4.6 to 5.7 times. It was found that using a photodiode (PD) with a flat window and a larger photosensitive surface can collect the Lambertian emission fluorescence in the flow cell more efficiently, increasing the signal-to-noise ratio of each channel 1.3 to 1.8 times. The limits of detection (LODs, 3 times peak-peak noise) of aflatoxin B2 (AFB2), ochratoxin (OTA), and zearalenone (ZEN) were 0.33, 1.80, and 28.2 ng/L, respectively. Finally, six mycotoxins were analyzed simultaneously by the detector coupling with HPLC. The results showed that the sensitivity of the detector was at the best level to date, which was better than that of the top commercial fluorescence detectors (FLDs). The developed detector has the advantages of having small volume, low cost, and long lifetime and being robust, which has wide application and market prospects.
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Affiliation(s)
- Chuanliang Wang
- Department of Instrumentation & Analytical Chemistry, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Key Laboratory of Deep-sea Composition Detection Technology of Liaoning Province, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Sha
- Department of Instrumentation & Analytical Chemistry, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Key Laboratory of Deep-sea Composition Detection Technology of Liaoning Province, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
- South-Central Minzu University, 182 Minyuan Road, Hongshan District, Wuhan 430074, China
| | - Jiashan Lu
- Department of Instrumentation & Analytical Chemistry, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Key Laboratory of Deep-sea Composition Detection Technology of Liaoning Province, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
| | - Yafeng Guan
- Department of Instrumentation & Analytical Chemistry, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Key Laboratory of Deep-sea Composition Detection Technology of Liaoning Province, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
- Institute of Deep-Sea Science & Engineering, CAS, 28 Luhuitou Road, Sanya 572000, China
| | - Xuhui Geng
- Department of Instrumentation & Analytical Chemistry, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Key Laboratory of Deep-sea Composition Detection Technology of Liaoning Province, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
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6
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Zhang Y, Liu S, Zhou X, Cheng J. Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology. Molecules 2024; 29:2968. [PMID: 38998920 PMCID: PMC11243293 DOI: 10.3390/molecules29132968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.
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Affiliation(s)
- Yurong Zhang
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Shuxian Liu
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Xianqing Zhou
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Junhu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
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7
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Dubal ÍTP, Coradi PC, Dos Santos Bilhalva N, Biduski B, Lutz É, Mallmann CA, Anschau KF, Flores EMM. Monitoring of carbon dioxide and equilibrium moisture content for early detection of physicochemical and morphological changes in soybeans stored in vertical silos. Food Chem 2024; 436:137721. [PMID: 37864969 DOI: 10.1016/j.foodchem.2023.137721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/05/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
In the context of grain storage, impurities and soybeans defects in soybeans can significantly impact the equilibrium moisture content. This, cause moisture migration and heating of the stored product, leading to increased respiratory activity. Furthermore, temperature measurements within stored grain mass do not provide sufficient information for effective grain quality monitoring, primarily due to the grains excellent thermal insulating properties. To address this issue, we propose a different approach: monitoring the equilibrium moisture content and CO2 concentration as indicators of soybean respiration within the intergranular spaces of the stored grain mass. This study propose monitoring the CO2 concentration in the intergranular air along with environmental variables for early detection of physicochemical and morphological changes in soybeans stored in vertical silos using near infrared spectroscopy, X-ray diffraction and scanning electron microscopy. Thermogravimetry and spectrometry analyses revealed that the interrelationships among variables had a direct impact on soybean quality attributes. Specifically, the presence of soybeans with 5.2 % impurities led to an increased in respiration rates, resulting in a CO2 concentration of up to 5000 ppm and the consumption of up to 3.6 % of dry matter. Consequently, there were changes in the percentage of ash, proteins, fibers, and oils compositions. These findings highlight the potential for indirect assessments, enabling the prediction of physicochemical quality and contamination of soybeans stored in vertical silos through continuous monitoring of CO2 concentration and equilibrium moisture content.
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Affiliation(s)
- Ítala Thaisa Padilha Dubal
- Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil
| | - Paulo Carteri Coradi
- Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil; Laboratory of Postharvest (LAPOS), Campus Cachoeira do Sul, Federal University of Santa Maria, 96506-322 Cachoeira do Sul, Rio Grande do Sul, Brazil.
| | - Nairiane Dos Santos Bilhalva
- Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil
| | - Bárbara Biduski
- Food Quality and Sensory Science Department, Teagasc Food Research Centre Ashtown, Dublin City D15 KN3K, Ireland
| | - Éverton Lutz
- Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil
| | - Carlos Augusto Mallmann
- Laboratory of Mycotoxicological Analyses (LAMIC), Federal University of Santa Maria, 97105-970, Santa Maria, Rio Grande do Sul, Brazil
| | - Kellen Francine Anschau
- Department of Chemical Engineering, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil
| | - Erico Marlon Moraes Flores
- Department of Chemical Engineering, Federal University of Santa Maria, 97105-900 Santa Maria, Rio Grande do Sul, Brazil
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8
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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9
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Zhang Y, Wang Y. Recent trends of machine learning applied to multi-source data of medicinal plants. J Pharm Anal 2023; 13:1388-1407. [PMID: 38223450 PMCID: PMC10785154 DOI: 10.1016/j.jpha.2023.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 01/16/2024] Open
Abstract
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
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10
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Nadimi M, Hawley E, Liu J, Hildebrand K, Sopiwnyk E, Paliwal J. Enhancing traceability of wheat quality through the supply chain. Compr Rev Food Sci Food Saf 2023; 22:2495-2522. [PMID: 37078119 DOI: 10.1111/1541-4337.13150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/02/2023] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.
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Affiliation(s)
- Mohammad Nadimi
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - Jing Liu
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
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11
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Lin Y, Ma J, Sun DW, Cheng JH, Wang Q. A pH-Responsive colourimetric sensor array based on machine learning for real-time monitoring of beef freshness. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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12
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Adnouni M, Jiang L, Zhang X, Zhang L, Pathare PB, Roskilly A. Computational modelling for decarbonised drying of agricultural products: Sustainable processes, energy efficiency, and quality improvement. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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13
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Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248648. [PMID: 36557781 PMCID: PMC9785524 DOI: 10.3390/molecules27248648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R2 = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R2 = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.
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14
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Chen G, Hou J, Liu C. A Scientometric Review of Grain Storage Technology in the Past 15 Years (2007-2022) Based on Knowledge Graph and Visualization. Foods 2022; 11:foods11233836. [PMID: 36496644 PMCID: PMC9740888 DOI: 10.3390/foods11233836] [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: 09/25/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Food storage helps to ensure the food consumption needs of non-agricultural populations and to respond to major natural disasters or other emergencies, and the application of food storage technology can reduce post-harvest food losses. However, there are still obvious shortcomings in coping with large grain losses. Therefore, quantitative analysis of the research hotspots and evolutionary trends of grain storage technology is important to help the development of grain storage technology. This article uses the Web of Science database from 2007 to 2022 as a data sample with the help of CiteSpace software to analyze the basic situation, research hotspots, and evolutionary trends to draw a series of relevant knowledge maps. Visual analysis revealed that the number of publications had grown rapidly since 2015. First, the Journal of Stored Products Research, Journal of Economic Entomology, and Journal of Agricultural and Food Chemistry, with citation frequencies of 929, 536, and 453, should be focused on in order to keep up with the latest research developments in this field. The United States, China, and Brazil occupy dominant positions in relation to grain storage technology studies in general. Purdue University, Kansas State University, and Agricultural Research Institute ranked the top three in terms of the number and centrality of publications. In terms of research hotspots, the centrality of temperature, insects, carbon dioxide, and quality were 0.16, 0.09, 0.08, and 0.08. It shows that the field of grain storage technology in recent years has focused on grain storage temperature, pest control, and grain storage quality research. From the perspective of the evolution trend, the life cycle of emergent words lasts for several years, after which the strength of emergent words slowly decreases and is replaced by new emergent words. Mortality was the first keyword to appear and remained from 2007 to 2011, indicating that research on fumigants and their toxicity, as well as pest mortality under air fumigation and chemical fumigation conditions, became more popular during this period. In recent years, new terms have emerged that had never been used before, such as "grain quality" (2019-2022) and "stability" (2020-2022). We can find that people pursue food quality more with the improvement of people's living standards. In this context, future research should seek more efficient, safe, economical, and environmentally friendly methods of grain storage and continuously improve the level of scientific grain storage.
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Affiliation(s)
- Guixiang Chen
- College of Civil Engineering, Henan University of Technology, Zhengzhou 450001, China
- Henan Key Laboratory of Grain Storage Facility and Safety, Zhengzhou 450001, China
- Henan International Joint Laboratory of Modern Green Ecological Storage System, Zhengzhou 450001, China
- Correspondence:
| | - Jia Hou
- College of Civil Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Chaosai Liu
- College of Civil Engineering, Henan University of Technology, Zhengzhou 450001, China
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15
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Ma P, Zhang Z, Jia X, Peng X, Zhang Z, Tarwa K, Wei CI, Liu F, Wang Q. Neural network in food analytics. Crit Rev Food Sci Nutr 2022; 64:4059-4077. [PMID: 36322538 DOI: 10.1080/10408398.2022.2139217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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Affiliation(s)
- Peihua Ma
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Zhikun Zhang
- CISPA Helmholtz Center for Information Security, Saarbrucken, Germany
| | - Xiaoxue Jia
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Xiaoke Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Zhi Zhang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Kevin Tarwa
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Cheng-I Wei
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Fuguo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Qin Wang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
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16
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Global niche shifts of rice and its weak adaptability to climate change. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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17
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, 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, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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18
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Yao W, Liu R, Zhang F, Li S, Huang X, Guo H, Peng M, Zhong G. Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy. Molecules 2022; 27:molecules27196294. [PMID: 36234831 PMCID: PMC9571819 DOI: 10.3390/molecules27196294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Aflatioxin B1 (AFB1) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB1-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB1 or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB1 in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (Rc2 = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB1 infection in the early stages.
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Affiliation(s)
- Wanqing Yao
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
| | - Ruanshan Liu
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Fengru Zhang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Shuang Li
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoxia Huang
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Hongwei Guo
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Mengxia Peng
- Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China
| | - Guohua Zhong
- Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China
- Correspondence: (W.Y.); (G.Z.); Tel.: +86-13750592371 (W.Y.); +86-20-85280308 (G.Z.)
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19
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Compression and reinforce variation with convolutional neural networks for hyperspectral image classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Liu Y, Li D, Li H, Jiang X, Zhu Y, Cao W, Ni J. Design of a Phenotypic Sensor About Protein and Moisture in Wheat Grain. FRONTIERS IN PLANT SCIENCE 2022; 13:881560. [PMID: 35599872 PMCID: PMC9120668 DOI: 10.3389/fpls.2022.881560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
A near-infrared (NIR) spectrometer can perceive the change in characteristics of the grain reflectance spectrum quickly and nondestructively, which can be used to determine grain quality information. The full-band spectral information of samples of multiple physical states can be measured using existing instruments, yet it is difficult for the full-band instrument to be widely used in grain quality detection due to its high price, large size, non-portability, and inability to directly output the grain quality information. Because of the above problems, a phenotypic sensor about grain quality was developed for wheat, and four wavelengths were chosen. The interference of noise signals such as ambient light was eliminated by the phenotypic sensor using the modulated light signal and closed sample pool, the shape and size of the incident light spot of the light source were determined according to the requirement for collecting the reflectance spectrum of the grain, and the luminous units of the light source with stable light intensity and balanced luminescence were developed. Moreover, the sensor extracted the reflectance spectrum information using a weak optical signal conditioning circuit, which improved the resolution of the reflectance signal. A grain quality prediction model was created based on the actual moisture and protein content of grain obtained through Physico-chemical analyses. The calibration test showed that the R2 of the relative diffuse reflectance (RDR) of all four wavelengths of the phenotypic sensor and the reflectance of the diffusion fabrics were higher than 0.99. In the noise level and repeatability tests, the standard deviations of the RDR of two types of wheat measured by the sensor were much lower than 1.0%, indicating that the sensor could accurately collect the RDR of wheat. In the calibration test, the root mean square errors (RMSE) of protein and moisture content of wheat in the Test set were 0.4866 and 0.2161%, the mean absolute errors (MAEs) were 0.6515 and 0.3078%, respectively. The results showed that the NIR phenotypic sensor about grain quality developed in this study could be used to collect the diffuse reflectance of grains and the moisture and protein content in real-time.
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Affiliation(s)
- Yiming Liu
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Donghang Li
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Huaiming Li
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Xiaoping Jiang
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Yan Zhu
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Weixing Cao
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
| | - Jun Ni
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing, China
- Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing, China
- Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing, China
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21
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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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22
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Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
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Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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23
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Rifna EJ, Pandiselvam R, Kothakota A, Subba Rao KV, Dwivedi M, Kumar M, Thirumdas R, Ramesh SV. Advanced process analytical tools for identification of adulterants in edible oils - A review. Food Chem 2022; 369:130898. [PMID: 34455326 DOI: 10.1016/j.foodchem.2021.130898] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/16/2021] [Accepted: 08/16/2021] [Indexed: 12/16/2022]
Abstract
This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.
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Affiliation(s)
- E J Rifna
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - R Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India.
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum 695 019, Kerala, India.
| | - K V Subba Rao
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Madhuresh Dwivedi
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR-Central Institute for Research on Cotton Technology, Matunga, Mumbai 400019, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science and Technology, PJTSAU, Telangana, India
| | - S V Ramesh
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India
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24
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Kumar R, Paul V, Pandey R, Sahoo RN, Gupta VK. Reflectance based non-destructive determination of colour and ripeness of tomato fruits. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2022; 28:275-288. [PMID: 35221583 PMCID: PMC8847509 DOI: 10.1007/s12298-022-01126-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 06/14/2023]
Abstract
UNLABELLED The preference and quality of tomato fruit are primarily determined by its apparent colour and appearance. Non-destructive and rapid methods for assessment of tomato colour and ripeness are therefore of immense significance. This study was conducted to identify reflectance-based indices and to develop models for the non-destructive determination of colour and ripeness (maturity) of tomato fruits. Tomato fruits of two varieties and two hybrids, representing different ripening stages were investigated. Fruits were either harvested directly from the plants or they were picked up from the lots stored at 25 °C. Reflectance from individual fruit was recorded in a spectrum ranging from 350 to 2500 nm. These fruits at different ripening stages were ranked on a relative ripening score (0.0-8.5). Obtained data (reflectance and ripening score) were subjected to chemometric analysis. In total, six models were developed. The first-best model was based on the index R521 (reflectance at wavelength 521 nm) i.e., y (colour/ripeness) = - 2.456 ln (x) - 1.093 where x is R521. This model had a root mean standard error of prediction (RMSEP) ≥ 0.86 and biasness = - 0.09. The second-best model y = 2.582 ln (x) - 0.805 was based on the index R546 (x) and had RMSEP ≥ 0.89 and biasness = 0.10. Models could bifurcate tomatoes into basic ripening stages and also red and beyond red tomato fruits from other stages across the varieties/hybrids and ripening conditions [for plant harvested (fresh) and stored (aged) fruits]. Findings will prove useful in developing simple and thereby cost-effective tools for rapid screening/sorting of tomato fruits based on their colour or ripeness not only for basic research (phenotyping) but also for the purpose of processing, value-addition, and pharmaceutical usages. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-022-01126-2.
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Affiliation(s)
- Rajeev Kumar
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
- Present Address: Division of Vegetable Production, ICAR-Indian Institute of Vegetable Research (IIVR), Varanasi, Uttar Pradesh 221 305 India
| | - Vijay Paul
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - Rakesh Pandey
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - R. N. Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - V. K. Gupta
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
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25
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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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26
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Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Pontonio E, Arora K, Dingeo C, Carafa I, Celano G, Scarpino V, Genot B, Gobbetti M, Di Cagno R. Commercial Organic Versus Conventional Whole Rye and Wheat Flours for Making Sourdough Bread: Safety, Nutritional, and Sensory Implications. Front Microbiol 2021; 12:674413. [PMID: 34322100 PMCID: PMC8312275 DOI: 10.3389/fmicb.2021.674413] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/10/2021] [Indexed: 01/04/2023] Open
Abstract
Organic farming is gaining a broad recognition as sustainable system, and consumer demand for organic products has increased dramatically in the recent past. Whether organic agriculture delivers overall advantages over conventional agriculture is, however, contentious. Here, the safety, nutritional, and sensory implications of using commercial organic rye, soft, and durum wheat flours rather than conventional-made sourdough bread have been investigated. Culture-dependent and culture-independent approaches were used to explore the microbial architecture of flours and to study their dynamics during sourdough propagation. Besides biochemical features, the main nutritional (amino acid content, asparagine level, and antioxidant activity) characteristics of sourdoughs were investigated, and their effect on the structural, nutritional, and sensory profiles of breads assessed. Overall, the organic farming system led to flours characterized by lower content of asparagine and cell density of Enterobacteriaceae while showing higher concentration of total free amino acids. Differences of the flours mirrored those of sourdoughs and breads. The use of sourdough fermentation guaranteed a further improvement of the flour characteristics; however, a microbial and sensory profile simplification as well as a slight decrease of the biochemical parameters was observed between breads with sourdough after one-cycle fermentation and 10 days of propagation.
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Affiliation(s)
- Erica Pontonio
- Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Bari, Italy
| | - Kashika Arora
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
| | - Cinzia Dingeo
- Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Bari, Italy
| | - Ilaria Carafa
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
| | - Giuseppe Celano
- Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Bari, Italy
| | - Valentina Scarpino
- Department of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco, Italy
| | | | - Marco Gobbetti
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
| | - Raffaella Di Cagno
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
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Farhadi A, Fakhri Y, Kachuei R, Vasseghian Y, Huseyn E, Mousavi Khaneghah A. Prevalence and concentration of fumonisins in cereal-based foods: a global systematic review and meta-analysis study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:20998-21008. [PMID: 33694116 DOI: 10.1007/s11356-021-12671-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
Cereal-based foods are utilized as an essential food segment worldwide. Nevertheless, their contamination by mycotoxins, also fumonisins, could pose a critical health risk. The present research provides the first systematic review regarding the prevalence and concentration of fumonisins in cereal-based food with the aid of a meta-analysis. In this regard, some international databases PubMed, Web of Science, Google Scholar, and Scopus were explored during the last 30 years. Among 9729 screened articles, 73 articles (which meet the proposed inclusion criteria), including 11,132 data, were incorporated in the performed meta-analysis. The overall rank order regarding the concentration of fumonisins in cereal-based foods was corn-based foods > wheat-based foods > other cereal foods > barley-based foods > rice-based foods > oat-based foods. Based on the prevalence of fumonisins, the overall rank order was other cereal foods > corn-based foods > rice-based foods > wheat-based foods > oat-based foods > barley-based food. The present meta-analysis results can be a beneficial database for risk assessment model progress, which can help industries and organizations decrease the presence of fumonisins in cereal-based food.
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Affiliation(s)
- Ahmad Farhadi
- Department of Clinical Sciences, Faculty of Veterinary Medicine, University of Tabriz, Tabriz, Iran
| | - Yadolah Fakhri
- Food Health Research Center, Department of Environmental Health Engineering, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Reza Kachuei
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Yasser Vasseghian
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
| | - Elcin Huseyn
- Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics, Azerbaijan State Oil and Industry University, 20 Azadlig Ave., AZ1010, Baku, Azerbaijan
| | - Amin Mousavi Khaneghah
- Department of Food Science and Nutrition, Faculty of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80. Caixa Postal: 6121, Campinas, São Paulo, CEP: 13083-862, Brazil.
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Fingerprinting and tagging detection of mycotoxins in agri-food products by surface-enhanced Raman spectroscopy: Principles and recent applications. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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30
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Müller-Maatsch J, Bertani FR, Mencattini A, Gerardino A, Martinelli E, Weesepoel Y, van Ruth S. The spectral treasure house of miniaturized instruments for food safety, quality and authenticity applications: A perspective. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.01.091] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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31
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Saha D, Manickavasagan A. Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Curr Res Food Sci 2021; 4:28-44. [PMID: 33659896 PMCID: PMC7890297 DOI: 10.1016/j.crfs.2021.01.002] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/15/2021] [Accepted: 01/26/2021] [Indexed: 11/29/2022] Open
Abstract
Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed. Artificial neural network has been intensively used for Hyperspectral image (HSI) analysis. Support vector machines and random forest techniques are gaining momentum for HSI analysis. Deep learning applications has potential for implementation in real time HSI analysis. Lifelong machine learning needs further research to incorporate the seasonal variations in food quality.
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Affiliation(s)
- Dhritiman Saha
- School of Engineering, University of Guelph, N1G2W1, Canada
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33
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Liang N, Sun S, Zhang C, He Y, Qiu Z. Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food. Crit Rev Food Sci Nutr 2020; 62:2963-2984. [PMID: 33345592 DOI: 10.1080/10408398.2020.1862045] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.
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Affiliation(s)
- Ning Liang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Sashuang Sun
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
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Wang Q, Li L, Zheng X. Recent advances in heat-moisture modified cereal starch: Structure, functionality and its applications in starchy food systems. Food Chem 2020; 344:128700. [PMID: 33248839 DOI: 10.1016/j.foodchem.2020.128700] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/04/2020] [Accepted: 11/18/2020] [Indexed: 10/22/2022]
Abstract
Cereals, one of the starch sources, have a tremendous and steady production worldwide. Starchy foods constitute the major part of daily calorie intake for humans. As a simple and green modification approach, heat-moisture treatment (HMT) could change the granular surface characteristics and size, crystalline and helical structure, as well as molecular organization of cereal starch. The changing degree is contingent on HMT parameters and botanical origin. Based on the hierarchical structure, this paper reviews functionalities of heat-moisture modified cereal starch (HMCS) reported in latest years. The functionality of HMCS could be affected by co-existing non-starch ingredients through non-covalent/covalent interactions, depolymerization or simply attachment/encapsulation. Besides, it summarizes the modulation of HMCS in dough rheology and final food products' quality. Selecting proper HMT conditions is crucial for achieving nutritious products with desirable sensory and storage quality. This review gives a systematic understanding about HMCS for the better utilization in food industry.
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Affiliation(s)
- Qingfa Wang
- College of Grain, Oil and Food Science, Henan University of Technology, No.100 Lianhua Street in Zhongyuan District, Zhengzhou, Henan 450001, China
| | - Limin Li
- College of Grain, Oil and Food Science, Henan University of Technology, No.100 Lianhua Street in Zhongyuan District, Zhengzhou, Henan 450001, China
| | - Xueling Zheng
- College of Grain, Oil and Food Science, Henan University of Technology, No.100 Lianhua Street in Zhongyuan District, Zhengzhou, Henan 450001, China.
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35
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Pontes JGDM, Fernandes LS, Dos Santos RV, Tasic L, Fill TP. Virulence Factors in the Phytopathogen-Host Interactions: An Overview. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:7555-7570. [PMID: 32559375 DOI: 10.1021/acs.jafc.0c02389] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Phytopathogens are responsible for great losses in agriculture, once they are able to subvert or elude the host defense mechanisms through virulence factors secretion for their dissemination. Herein, it is reviewed phytotoxins that act as virulence factors and are produced by bacterial phytopathogens (Candidatus Liberibacter spp., Erwinia amylovora, Pseudomonas syringae pvs and Xanthomonas spp.) and fungi (Alternaria alternata, Botrytis cinerea, Cochliobolus spp., Fusarium spp., Magnaporthe spp., and Penicillium spp.), which were selected in accordance to their worldwide importance due to the biochemical and economical aspects. In the current review, it is sought to understand the role of virulence factors in the pathogen-host interactions that result in plant diseases.
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Affiliation(s)
| | - Laura Soler Fernandes
- Laboratório de Biologia Quı́mica Microbiana (LaBioQuiMi), IQ-UNICAMP, Campinas, SP, Brazil
| | | | - Ljubica Tasic
- Laboratório de Quı́mica Biológica (LQB), IQ-UNICAMP, Campinas, SP, Brazil
| | - Taicia Pacheco Fill
- Laboratório de Biologia Quı́mica Microbiana (LaBioQuiMi), IQ-UNICAMP, Campinas, SP, Brazil
- Institute of Chemistry, Universidade Estadual de Campinas (UNICAMP), P.O. Box 6154, 13083970 Campinas, SP, Brazil
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36
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Li X, Zhang L, Zhang Y, Wang D, Wang X, Yu L, Zhang W, Li P. Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.05.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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37
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Azam SMR, Ma H, Xu B, Devi S, Siddique MAB, Stanley SL, Bhandari B, Zhu J. Efficacy of ultrasound treatment in the removal of pesticide residues from fresh vegetables: A review. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.01.028] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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