Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review
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Muhammad Zareef | Huanhuan Li | Mehedi Hassan | Qin Ouyang | Waqas Ahmad | Quansheng Chen | Muhammad Arslan | Malik Muhammad Hashim | Shujat Ali | Xiangyang Wu | Huanhuan Li | Quansheng Chen | Qin Ouyang | Xiangyang Wu | W. Ahmad | Shujat Ali | M. Zareef | Muhammad Arslan | Mehedi Hassan | M. M. Hashim
[1] Luis Cuadros-Rodríguez,et al. Quality performance metrics in multivariate classification methods for qualitative analysis , 2016 .
[2] Rodinei Augusti,et al. Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers. , 2016, Analytica chimica acta.
[3] Kamila Kochan,et al. Raman and infrared spectroscopy of carbohydrates: A review. , 2017, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[4] W. Ahmad,et al. Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology , 2020 .
[5] Yvan Vander Heyden,et al. A review on the application of chromatographic methods, coupled to chemometrics, for food authentication , 2018, Food Control.
[6] F. Arslan,et al. Attenuated Total Reflectance–Fourier Transform Infrared (ATR–FTIR) Spectroscopy Combined with Chemometrics for Rapid Determination of Cold-Pressed Wheat Germ Oil Adulteration , 2018, Food Analytical Methods.
[7] Zhihong Shi,et al. Development of multi-residue analysis of herbicides in cereal grain by ultra-performance liquid chromatography-electrospray ionization-mass spectrometry. , 2016, Food chemistry.
[8] C. Pasquini. Near infrared spectroscopy: A mature analytical technique with new perspectives - A review. , 2018, Analytica chimica acta.
[9] M. Dramićanin,et al. Characterization of cereal flours by fluorescence spectroscopy coupled with PARAFAC. , 2017, Food chemistry.
[10] Quansheng Chen,et al. Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms , 2018 .
[11] Y. Ozaki,et al. Simple and rapid determination of free fatty acids in brown rice by FTIR spectroscopy in conjunction with a second-derivative treatment. , 2016, Food chemistry.
[12] Huanhuan Li,et al. Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia). , 2021, Food chemistry.
[13] Nisar Hussain,et al. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications , 2019, Trends in Food Science & Technology.
[14] A. Baker,et al. Fluorescence spectroscopy for wastewater monitoring: A review. , 2016, Water research.
[15] Santosh Lohumi,et al. Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy , 2016 .
[16] Lembe S. Magwaza,et al. Destructive and non-destructive techniques used for quality evaluation of nuts: A review , 2019, Scientia Horticulturae.
[17] Ling Zhu,et al. Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy , 2018, Journal of Cereal Science.
[18] Paul J. Williams,et al. Near infrared hyperspectral imaging in quality and safety evaluation of cereals , 2018, Critical reviews in food science and nutrition.
[19] Muhammad Zareef,et al. Rapid screening of phenolic compounds in congou black tea ( Camellia sinensis ) during in vitro fermentation process using portable spectral analytical system coupled chemometrics , 2019, Journal of Food Processing and Preservation.
[20] Determining moisture content in pasta by vibrational spectroscopy. , 2018, Talanta.
[21] Yuying Jiang,et al. Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning. , 2020, Food chemistry.
[22] Quan-Sheng Chen,et al. A novel colorimetric sensor array based on boron-dipyrromethene dyes for monitoring the storage time of rice. , 2018, Food chemistry.
[23] A. Fabbri,et al. Evaluation of drying of edible coating on bread using NIR spectroscopy , 2019, Journal of Food Engineering.
[24] Zou Xiaobo,et al. Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques , 2021 .
[25] Muhammad Zareef,et al. Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms. , 2019, Journal of the science of food and agriculture.
[26] Rasmus Bro,et al. Recent chemometrics advances for foodomics , 2017 .
[27] M. Vázquez-Hernández,et al. Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products. , 2020, Drug discovery today.
[28] Zou Xiaobo,et al. Colorimetric sensor arrays based on chemo-responsive dyes for food odor visualization , 2018, Trends in Food Science & Technology.
[29] Nicola Caporaso,et al. Protein content prediction in single wheat kernels using hyperspectral imaging , 2018, Food chemistry.
[30] Xuesong Jiang,et al. On-line detection of toxigenic fungal infection in wheat by visible/near infrared spectroscopy , 2019, LWT.
[31] Seung-Chul Yoon,et al. Detection of aflatoxin B 1 (AFB 1 ) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging , 2017 .
[32] Soledad Cerutti,et al. Applications of liquid-phase microextraction procedures to complex samples assisted by response surface methodology for optimization , 2020 .
[33] Su-yeon Kim,et al. Prediction of warmed-over flavour development in cooked chicken by colorimetric sensor array. , 2016, Food chemistry.
[34] C. Dogan,et al. Detection of l-Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA. , 2017, Food Chemistry.
[35] Jian Liu,et al. A new methodology for sensory quality assessment of garlic based on metabolomics and an artificial neural network , 2019, RSC advances.
[36] Yuan Zhang,et al. THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion , 2018, Sensors.
[37] Guohua Zhao,et al. Rapid determination of farinograph parameters of wheat flour using data fusion and a forward interval variable selection algorithm , 2017 .
[38] Qin Ouyang,et al. Rapid sensing of total theaflavins content in black tea using a portable electronic tongue system coupled to efficient variables selection algorithms , 2019, Journal of Food Composition and Analysis.
[39] Rafael Rieder,et al. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review , 2018, Comput. Electron. Agric..
[40] D. Jayas,et al. Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging , 2017 .
[41] Colm P. O'Donnell,et al. Applications of fluorescence spectroscopy in dairy processing: a review , 2017 .
[42] R. Alfenas,et al. Comparing sorghum and wheat whole grain breakfast cereals: Sensorial acceptance and bioactive compound content. , 2017, Food chemistry.
[43] Zou Xiaobo,et al. Non-invasive sensing for food reassurance. , 2016, The Analyst.
[45] Jiewen Zhao,et al. Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model , 2017 .
[46] Daniel Cozzolino,et al. Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review , 2019, Postharvest Biology and Technology.
[47] A. De Girolamo,et al. Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran. , 2018, Journal of the science of food and agriculture.
[48] Zou Xiaobo,et al. Total polyphenol quantitation using integrated NIR and MIR spectroscopy: A case study of Chinese dates (Ziziphus jujuba). , 2019, Phytochemical analysis : PCA.
[49] Ana M. Jiménez-Carvelo,et al. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. , 2019, Food research international.
[50] Baojun Xu,et al. Application of vibrational spectroscopy for classification, authentication and quality analysis of mushroom: A concise review. , 2019, Food chemistry.
[51] Susithra Lakshmanan,et al. Colorimetric sensors for rapid detection of various analytes. , 2017, Materials science & engineering. C, Materials for biological applications.
[52] Ching-Lu Hsieh,et al. Measurement of moisture content for rough rice by visible and near-infrared (NIR) spectroscopy , 2016 .
[53] Yong He,et al. Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey , 2020 .
[54] Terry F. McGrath,et al. Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[55] Hong-Ju He,et al. Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review , 2016, Critical reviews in food science and nutrition.
[56] A. Dankowska,et al. Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[57] A. Kulmyrzaev,et al. Study of moisture content and water activity of rice using fluorescence spectroscopy and multivariate analysis. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[58] Abdo Hassoun,et al. Fluorescence spectroscopy as a rapid and non-destructive method for monitoring quality and authenticity of fish and meat products: Impact of different preservation conditions , 2019, LWT.
[59] Weibiao Zhou,et al. Design of experiments and regression modelling in food flavour and sensory analysis: A review , 2018 .
[60] Q. Hu,et al. Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology , 2020, International Journal of Food Science & Technology.
[61] J. Hinrichs,et al. Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics , 2016 .
[62] Itziar Ruisánchez,et al. FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud. , 2016, Talanta.
[63] Ozgur Kisi,et al. Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm , 2020, Natural Hazards.
[64] Hakil Kim,et al. A critical review on computer vision and artificial intelligence in food industry , 2020, Journal of Agriculture and Food Research.
[65] Quansheng Chen,et al. Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy. , 2019, Food chemistry.
[66] Hao Lin,et al. Discrimination of aged rice using colorimetric sensor array combined with volatile organic compounds , 2019, Journal of Food Process Engineering.
[67] Quansheng Chen,et al. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis , 2020, Food Engineering Reviews.
[68] Jânio Sousa Santos,et al. Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing. , 2018, Comprehensive reviews in food science and food safety.
[69] Jiewen Zhao,et al. Determination of Rice Storage Time with Colorimetric Sensor Array , 2017, Food Analytical Methods.
[70] Felix Y.H. Kutsanedzie,et al. Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for ochratoxin A and aflatoxin B1 detection. , 2020, Food chemistry.
[71] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[72] Da-Wei Li,et al. Determination and quality evaluation of green tea extracts through qualitative and quantitative analysis of multi-components by single marker (QAMS). , 2016, Food chemistry.
[73] Abdul Rohman,et al. The employment of Fourier transform infrared spectroscopy coupled with chemometrics techniques for traceability and authentication of meat and meat products , 2018, Journal of advanced veterinary and animal research.
[74] D. Cozzolino. An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals , 2014 .
[75] Quansheng Chen,et al. Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. , 2018, Food chemistry.
[76] P. Koczoń,et al. The Application of FT-IR Spectroscopy for Quality Control of Flours Obtained from Polish Producers , 2017, Journal of analytical methods in chemistry.
[77] Quansheng Chen,et al. Qualitative identification of rice actual storage period using olfactory visualization technique combined with chemometrics analysis , 2020 .
[78] A. Malik,et al. A Review on Recent Applications of High-Performance Liquid Chromatography in Metal Determination and Speciation Analysis , 2017, Critical reviews in analytical chemistry.
[79] H. Mishra,et al. Rapid Assessment of Quality Change and Insect Infestation in Stored Wheat Grain Using FT-NIR Spectroscopy and Chemometrics , 2018, Food Analytical Methods.
[80] Da-Wen Sun,et al. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications , 2017 .
[81] Yan-wen Wu,et al. Rapid screening of mineral oil aromatic hydrocarbons (MOAH) in grains by fluorescence spectroscopy. , 2019, Food chemistry.
[82] Vahid Esfahanian,et al. Reduced‐order modeling of lead‐acid battery using cluster analysis and orthogonal cluster analysis method , 2019, International Journal of Energy Research.
[83] Digvir S. Jayas,et al. Detection of Broken Kernels Content in Bulk Wheat Samples Using Near-Infrared Hyperspectral Imaging , 2016, Agricultural Research.
[84] R. Krska,et al. A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits , 2016, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.
[85] Chao-Yin Tsai,et al. Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics , 2020, Sensors.
[86] Michael Pérez-Rodríguez,et al. Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion. , 2021, Food chemistry.
[87] Daniel Cozzolino,et al. Classification and Authentication of Barley (Hordeum vulgare) Malt Varieties: Combining Attenuated Total Reflectance Mid-infrared Spectroscopy with Chemometrics , 2017, Food Analytical Methods.
[88] H. Susi,et al. Resolution-enhanced Fourier transform infrared spectroscopy of enzymes. , 1986, Methods in enzymology.
[89] B. Hammock,et al. Development of a Nanobody-AviTag Fusion Protein and Its Application in a Streptavidin-Biotin-Amplified Enzyme-Linked Immunosorbent Assay for Ochratoxin A in Cereal. , 2018, Analytical chemistry.
[90] Z. Wang,et al. Rapid determination of the texture properties of cooked cereals using near-infrared reflectance spectroscopy , 2018, Infrared Physics & Technology.