Rapid detection and quantification of sucrose adulteration in cow milk using Attenuated total reflectance-Fourier transform infrared spectroscopy coupled with multivariate analysis

Balan, Biji; Dhaulaniya, Amit S.; Jamwal, Rahul; Yadav, A. K.; Kelly, Simon; Cannavan, Andrew; Singh, Dileep Kumar

doi:10.1016/j.saa.2020.118628

Cited by 37 publications

(18 citation statements)

References 35 publications

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“…The given result shows that increasing N leads to l increase, and satisfy the formula: l = 18.021 − 138.153N −1/3 , corresponding to l~N −1/3 (Figure 5a) and −E tot proportional to N (Figure 5b). The results are consistent with the results of crystallizing process temperature (T m ) proportional with N −1/3 [74,75] and size (l or D) proportional with N −1/3 [67][68][69][70][71][72][73]. This proves that increasing atom number leads to crystalline atoms number FCC, HCP increase, Amor decrease, size increase, the total energy of system decrease; and the relationship with l~N −1/3 is an important result for future experimental implementation.…”

Section: Effect Of Atomic Numbersupporting

confidence: 91%

“…Berendsen et al [65] have identified the transition temperature (T m ) of NiAu ranges from T m = 1100 K to T m = 1300 K with Au impurity concentration of 58% [66]. Combined with our recent results as Al [67], FeNi [68], AlNi [69], Ni 1−x Fe x [70], Ni 1−x Cu x [71], Ni [72,73], these results show that the transition temperature (T m ) of Ni material; T m is always proportional with atom number (N), N −1/3 [74,75], and the electronic structure of AuCu [76] and AgAu [77]. The phase transition of Ni material can be determined by stress or temperature [78][79][80][81], and the bonding length of Ni-Ni determined by the experimental method is r = 2.43 Å [82], while the simulation method of Dung, N.T is r = 2.45 Å [73], and P.H.…”

Section: Introductionsupporting

confidence: 79%

“…After studying the effect of factors on the structure and crystallize process, we got the following results: The relationship between size (l) and atomic number (N) is determined according to the formula is l~N −1/3 ; the total energy of the system (−E tot ) always depends on N by function, the result is consistent with results [67][68][69][70][71][72][73][74][75] when the glass temperature (T g ), T g = 600 K. With NiAu 5324, when increasing the heating rate from 4 × 10 12 K/s to 2 × 10 13 K/s, 4 × 10 13 K/s, 2 × 10 14 K/s, and 4 × 10 14 K/s leads to r decrease from r = 2.47 Å to r = 2.43 Å, and g(r) decrease from g(r) = 6.51 to g(r) = 6.05, corresponding to the change of g(r) and structural unit number. FCC, HCP, Amor FCC decreases from 802 FCC to 0.0 FCC, HCP decreases from 811 HCP to 13 HCP, Amor increases from 3711 Amor to 5311 Amor.…”

Section: Discussionsupporting

confidence: 60%

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Section: Effect Of Atomic Numbersupporting

confidence: 91%

Section: Introductionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 60%

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“…Table 2 shows the statistical parameters used to assess the quality of the calibration model and evaluate the performance of its prediction [ 31 , 32 ]. For model evaluation, the following statistical parameters were used, including the coefficient of determination of calibration and cross-validation (R 2 c and R 2 cv ), root means squared errors of calibration, and cross-validation (RMSEC and RMSECV), and the ratio of prediction to deviation in cross-validation (RPD cv ).…”

Section: Methodsmentioning

confidence: 99%

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Yulia

Suhandy

2021

Molecules

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In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

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“…Various groups have developed NIR spectroscopy methods for quality control of milk samples [192,[199][200][201][202]. Milk, the principal consumed dairy product, could be adulterated in several ways by addition of different substances such as urea, hydrogen peroxide, vegetable proteins, water, and others [203,204].…”

Section: Dairy Productsmentioning

confidence: 99%

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

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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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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Rapid detection and quantification of sucrose adulteration in cow milk using Attenuated total reflectance-Fourier transform infrared spectroscopy coupled with multivariate analysis

Cited by 37 publications

References 35 publications

Multifunctional Composites

Multifunctional Composites

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

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

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