Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy

Lohumi, Santosh; Kandpal, Lalit Mohan; Seo, Young Wook; Cho, Byoung‐Kwan

doi:10.5307/jbe.2016.41.3.208

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Thus, these variations may reduce the performance of developed multivariate analysis models. Therefore, the calibration model should be tested with an external unknown validation data to justify the reproducibility of the calibration model [ 15 , 52 ]. For this reason, additional experimental data was imaged in two groups as a blind set that was not included in the calibrations set.…”

Section: Resultsmentioning

confidence: 99%

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Faqeerzada

Lohumi

Kim

et al. 2020

Sensors

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The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

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Section: Resultsmentioning

confidence: 99%

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Faqeerzada

Lohumi

Kim

et al. 2020

Sensors

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“…The FT-NIR spectra of three different types of powdered samples, presented in Figure 2 b, shows that the absorption intensities ranged from 5208 to 5050 cm –1 , representing easily visualized peaks formed as a result of the O−H stretch and O−H band combination and the H−O−H deformation combination, corresponding to the starch content [ 31 , 32 ]. The spectral differences were observed from 7142–6250 cm –1 , corresponding to the first overtone of the hydroxyl group.…”

Section: Resultsmentioning

confidence: 99%

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Faqeerzada

Lohumi

Joshi

et al. 2020

Foods

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Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

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“…In contrast to the above methods, multivariate analysis employs full spectra and can thus be adopted when selective information is not available [12]. Although the application of this method for both qualitative and quantitative imaging has been reviewed by [1,13], most of the provided examples correspond to pharmaceutical and biomedical domains.…”

Section: Introductionmentioning

confidence: 99%

Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods

et al. 2018

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Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector.

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Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy

Cited by 8 publications

References 36 publications

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods

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