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Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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Bala M, Sethi S, Sharma S, Mridula D, Kaur G. Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:1294-1302. [PMID: 36098480 DOI: 10.1002/jsfa.12223] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/08/2022] [Accepted: 09/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. RESULTS The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. CONCLUSION The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Manju Bala
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Swati Sethi
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Sanjula Sharma
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - D Mridula
- Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
| | - Gurpreet Kaur
- Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
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Rovira G, Miaw CSW, Martins MLC, Sena MM, de Souza SVC, Callao MP, Ruisánchez I. One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts. Talanta 2023; 253:123916. [PMID: 36126522 DOI: 10.1016/j.talanta.2022.123916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/01/2022] [Accepted: 09/03/2022] [Indexed: 12/15/2022]
Abstract
A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.
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Affiliation(s)
- Glòria Rovira
- Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira I Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain
| | - Carolina Sheng Whei Miaw
- Department of Food Science, Faculty of Pharmacy (FAFAR), Federal University of Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Campus da UFMG, Pampulha, 31270-010, Belo Horizonte, MG, Brazil
| | - Mário Lúcio Campos Martins
- Department of Food Science, Faculty of Pharmacy (FAFAR), Federal University of Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Campus da UFMG, Pampulha, 31270-010, Belo Horizonte, MG, Brazil
| | - Marcelo Martins Sena
- Chemistry Department, Institute of Exact Sciences (ICEX), Federal University of Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Campus da UFMG, Pampulha, 31270-010, Belo Horizonte, MG, Brazil; Instituto Nacional de Ciência e Tecnologia em Bioanalítica (INCT-Bio), Campinas, SP, 13083-970, Brazil
| | - Scheilla Vitorino Carvalho de Souza
- Department of Food Science, Faculty of Pharmacy (FAFAR), Federal University of Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Campus da UFMG, Pampulha, 31270-010, Belo Horizonte, MG, Brazil
| | - M Pilar Callao
- Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira I Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
| | - Itziar Ruisánchez
- Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira I Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain
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Haruna SA, Li H, Wei W, Geng W, Luo X, Zareef M, Yao-Say Solomon Adade S, Ivane NMA, Isa A, Chen Q. Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121854. [PMID: 36162210 DOI: 10.1016/j.saa.2022.121854] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/14/2022] [Accepted: 09/03/2022] [Indexed: 06/16/2023]
Abstract
Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Adamu Isa
- Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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Liu HY, Wadood SA, Xia Y, Liu Y, Guo H, Guo BL, Gan RY. Wheat authentication:An overview on different techniques and chemometric methods. Crit Rev Food Sci Nutr 2021; 63:33-56. [PMID: 34196234 DOI: 10.1080/10408398.2021.1942783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.
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Affiliation(s)
- Hong-Yan Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Syed Abdul Wadood
- Department of Food and Nutrition, University of Home Economics, Lahore, Pakistan
| | - Yu Xia
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Yi Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Huan Guo
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Bo-Li Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ren-You Gan
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China.,Key Laboratory of Coarse Cereal Processing (Ministry of Agriculture and Rural Affairs), Sichuan Engineering & Technology Research Center of Coarse Cereal Industrialization, Chengdu University, Chengdu, China
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Bwambok DK, Siraj N, Macchi S, Larm NE, Baker GA, Pérez RL, Ayala CE, Walgama C, Pollard D, Rodriguez JD, Banerjee S, Elzey B, Warner IM, Fakayode SO. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6982. [PMID: 33297345 PMCID: PMC7730680 DOI: 10.3390/s20236982] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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Affiliation(s)
- David K. Bwambok
- Chemistry and Biochemistry, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA;
| | - Noureen Siraj
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Samantha Macchi
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Nathaniel E. Larm
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Gary A. Baker
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Rocío L. Pérez
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Caitlan E. Ayala
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Charuksha Walgama
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - David Pollard
- Department of Chemistry, Winston-Salem State University, 601 S. Martin Luther King Jr Dr, Winston-Salem, NC 27013, USA;
| | - Jason D. Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave., St. Louis, MO 63110, USA;
| | - Souvik Banerjee
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - Brianda Elzey
- Science, Engineering, and Technology Department, Howard Community College, 10901 Little Patuxent Pkwy, Columbia, MD 21044, USA;
| | - Isiah M. Warner
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
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Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools. Foods 2020; 9:foods9111551. [PMID: 33120902 PMCID: PMC7693377 DOI: 10.3390/foods9111551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/16/2020] [Accepted: 10/23/2020] [Indexed: 12/15/2022] Open
Abstract
Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.
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