Dataset of adulteration with water in coconut milk using FTIR spectroscopy

Sitorus, Agustami; Muslih, Muhammad; Cebro, Irwin Syahri; Bulan, Ramayanty

doi:10.1016/j.dib.2021.107058

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…The SNV processing converted each spectrum to a zero‐mean intensity or absorbance value with a standard deviation of one [32]. SNV was used to eliminate slope variation and to correct scatter effects in terms of light scattering and changes in light path length [33].…”

Section: Resultsmentioning

confidence: 99%

Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

Shi

et al. 2021

eFood

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Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of Enterobacter sp, Acetobacter pasteurianus, and Lactobacillus paracasei colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.

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Section: Resultsmentioning

confidence: 99%

Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

Shi

et al. 2021

eFood

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“…In several works, MIR was used to quantify the amount of cow's milk in more expensive milk types: buffalo [83,84], goat [85], and horse [86]. It was used to analyze coconut milk samples adulterated with water [87]. MIR spectrometers equipped with an ATR cell were employed to detect soya bean oil and common sugar [88], sucrose [89], and formalin [13] in cow's milk.…”

Section: Regressionmentioning

confidence: 99%

“…Moreover, for this reason, PLS has been habitually preferred to PCR. In some of the papers inspected for this review, these two methods were compared: on three occasions PLS provided the best prediction performances [13,86,89], whereas in one case the results obtained by the two methods were similar [87].…”

Section: Regressionmentioning

confidence: 99%

How Chemometrics Can Fight Milk Adulteration

Grassi

Tarapoulouzi

D’Alessandro

et al. 2022

Foods

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Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

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“…The model's performance was analyzed based on calibration and validation results according to the coefficient correlation of calibration, root means a square error of calibration (RMSEC), and crossprediction (RMSEP) [21,22]. Additionally, the prediction-todeviation ratio (RPD) was evaluated [23].…”

Section: Plsr Algorithm and Machine Learning Algorithmmentioning

confidence: 99%

A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

Munawar¹,

Sufardi²,

Yusmanizar³

et al. 2022

MMEP

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Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alpha-pinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel n-SVM algorithm is better than PLSR for myristicin prediction with gamma (g), c, and nu (n) of 0.1, 1.0, and 0.99, respectively. Also, the e-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (g), c, and epsilon (e) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.

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Dataset of adulteration with water in coconut milk using FTIR spectroscopy

Cited by 12 publications

References 14 publications

Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

How Chemometrics Can Fight Milk Adulteration

A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

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