Quick vacuum drying of liquid samples prior to <scp>ATR</scp>‐<scp>FTIR</scp> spectral collection improves the quantitative prediction: a case study of milk adulteration

Ayvaz, Hüseyin; Temizkan, Riza

doi:10.1111/ijfs.13839

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The spectral data contains the frequency‐doubling information of stretching and bending vibration of C–H bonds in various groups in this region, that is, combination C–H stretching within the scope of 1,300–1,420 nm, the first overtone of N–H and O–H stretching over the range of 1,400–1,500 nm, and the first overtone of C–H stretching in the range of 1,650–1,800 nm (Derrick, Stulik, & Landry, 1999). There was a strong and wide absorption peak over the range of 1,400–1,500 nm due to the expansion and contraction of the O–H bond in water molecules (Ayvaz & Temizkan, 2018). To eliminate the influence of water absorption in this range, the spectral data over the range of 1,400–1,500 nm were removed in further analysis.…”

Section: Resultsmentioning

confidence: 99%

Detecting melamine‐adulterated raw milk by using near‐infrared transmission spectroscopy

Liang

Zhu

Yang

et al. 2021

J Food Process Engineering

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Chinese national standards regulate that the melamine in infant food should not exceed 1 mg/kg. To quickly and accurately identify whether the melamine in raw milk exceeds the limit, near-infrared transmission (NIRT) spectra of 220 raw milk samples and 380 melamine-adulterated raw milk samples were collected over the spectral range of 900-1,700 nm. Different spectral preprocessing and dimension reduction methods were used to preprocess the data of full spectra with 425 wavelengths. The qualitative prediction models of partial least squares discriminate analysis (PLS-DA) and support vector machine (SVM) were built based on full spectra and reduced data.The results showed that the SVM was better than PLS-DA in identifying the melamine-adulterated raw milk whose melamine contents exceeded the limit of 1 mg/kg. The established SVM model using full spectra had the optimal prediction performance with the accuracy rate of 92.67%, sensitivity of 89.09%, and specificity of 94.74%. The study indicates that the raw milk whose melamine contents exceed the limit of 1 mg/kg could be identified by using NIRT spectroscopy. Practical ApplicationsThis study demonstrated that the raw milk whose melamine contents exceed the limit of 1 mg/kg could be identified by using near-infrared transmission spectroscopy.The results provide technical support and effective information for developing portable instrument to detect melamine-adulterated raw milk in situ or in field.

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Section: Resultsmentioning

confidence: 99%

Detecting melamine‐adulterated raw milk by using near‐infrared transmission spectroscopy

Liang

Zhu

Yang

et al. 2021

J Food Process Engineering

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“…In previous studies, discrimination was performed for only raw or pasteurized milks. 9,30 Also, different from the study of Amjad et al, in which the discrimination of milk species according to their species as cow, goat, buffalo, or human by using Raman spectroscopy, 14 the present study incorporates ewe milk instead of buffalo and human milk. In addition, the laser wavelength of the Raman spectroscopy used in the present study was 785 nm, whereas it was 532 nm in that study.…”

Section: Discrimination According To Heat Treatment By Using Pls-damentioning

confidence: 87%

“…Attenuated total reflectance Fourier transform infrared spectroscopy, Fourier transform NIR spectroscopy, NIR spectroscopy, and laser-induced breakdown spectroscopy were used for detecting the origins of cow, goat, sheep, and camel milk. [9][10][11][12][13] In these methods, quantitative chemometric methods were used and cow milk adulteration levels were determined. To the best of our knowledge, there is only one study conducted on determination of milk origins by using Raman spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

et al. 2020

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BACKGROUND Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS‐DA), first with regard to whether the milk was heat‐treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk. RESULTS First, discrimination of milk samples as raw or pasteurized was achieved using PLS‐DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples. CONCLUSION The results suggest that Raman spectroscopy coupled with PLS‐DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry

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“…In recent years, increasing attention has been paid to the application of spectroscopic techniques, namely vibrational spectroscopy, infrared spectroscopy, and Raman spectroscopy in food, animal science, and agriculture [10]. Attenuated total reflectance Fourier-transform infrared spectroscopy, Raman spectroscopy, near-infrared spectroscopy, and laser-induced breakdown spectroscopy have been used for detecting the adulteration in buffalo milk, goat milk, and camel milk [4,[11][12][13][14], and have shown good predictive potential. Mid-infrared spectroscopy (MIRS) is a real-time, sensitive, fast, green, high-throughput, clean, and low-cost biochemical fingerprinting technique that does not require sample preparation, and can give results within one minute [15].…”

Section: Introductionmentioning

confidence: 99%

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

Chu,

Wang,

Luo

et al. 2023

Foods

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Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

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Quick vacuum drying of liquid samples prior to ATR‐FTIR spectral collection improves the quantitative prediction: a case study of milk adulteration

Cited by 5 publications

References 35 publications

Detecting melamine‐adulterated raw milk by using near‐infrared transmission spectroscopy

Detecting melamine‐adulterated raw milk by using near‐infrared transmission spectroscopy

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

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