Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

Using machine learning models to predict the quality of plant-based foods

Plant-based foods (PBFs) are considered healthy, especially, minimally processed whole foods, fruits, whole grains, and legumes while highly processed PBFs maybe less nutritious. Educating consumers on nurient quality will help to guide their choices. This study was aimed at estimating and predicting the nutrient quality of PBFs using their Nutri-Score and micronutrient content. The NHANES (2017-2020) data shows the output for foods consumed in the US and their nutrient composition based on a 24-h recall. Though the Nutri-Score label has been used to discriminate food quality, it still needs to be implemented in most countries. It computes mostly macronutrients with less consideration for micronutrients which also contributes to product quality. ML methods used in this study combine the Nutri-Score grade and micronutrient content in predicting food quality. The FNDDS data of PBFs for 2017-2020 were split into training (n = 300) and testing (n = 74) datasets. Eight ML models were used to predict the Nutri-Score and the Nutri-Score grade of PBFs. Random forest (RF) and light gradient boost model (LightGBM) performed best with accuracy and coefficient of determination (R2) scores of 0.88 and 0.96, respectively, while DT had the least scores in predicting the Nutri-Score grade (0.81) and Nutri-Score (0.93). These results suggest that ML can be effectively leveraged to predict PBFs quality.

Validation of the Methods for the Non-Milk Fat Detection in Artificially Adulterated Milk with Palm Oil

Dairy products are among the most adulterated food products. Due to the current high price of milk fat, it has been replaced by low-cost oils, especially those oils that have the same fatty acid profile as milk fat. This study intends to confirm the lowest level of palm oil added to milk and validate various methods for detecting palm oil in milk, including gas chromatography, reverse-phase high performance liquid chromatography, and Fourier transform infrared. Different amounts of palm oil were prepared in the final liquid milk using five treatments of fresh milk cream and an emulsion of palm oil. The results of this study showed that the values of the saponification number decreased with the increase in the percentages of added palm oil. There was no decrease under the limits of the Egyptian standards until the addition of 50% palm oil. The iodine number is less sensitive than the saponification number in the detection of palm oil. Butyro refractometer reading is unable to detect the palm oil in milk. The fatty acid profile in milk determined by gas chromatography correlated well with the addition of palm oil. Furthermore, there is a positive relationship between the level of added palm oil and the β-sitosterol content as measured by reverse-phase high-performance liquid chromatography. There was no relationship between the behavior of the spectra resulting from Fourier transform infrared spectroscopy and the presence of palm oil.

Classification of Honey Powder Composition by FTIR Spectroscopy Coupled with Chemometric Analysis

Fourier transform infrared spectroscopy (FTIR) in connection with chemometric analysis were used as a fast and direct approach to classify spray dried honey powder compositions in terms of honey content, the type of diluent (water or skim milk), and carrier (maltodextrin or skim milk powder) used for the preparation of feed solutions before spray drying. Eleven variants of honey powders containing different amounts of honey, the type of carrier, and the diluent were investigated and compared to pure honey and carrier materials. Chemometric discrimination of samples was achieved by principal component analysis (PCA), hierarchical clustering analysis (HCA), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-750 cm^-1) and the extended region (3600-750 cm^-1). As a result, it was noticed that the type of carrier is a significant factor during the classification of different samples of powdered multifloral honey. PCA divided the samples based on the type of carrier, and additionally among maltodextrin-honey powders it was possible to distinguish the type of diluent. The result obtained by PCA-LDA and PLS-DA scores yielded a clear separation between four classes of samples and showed a very good discrimination between the different honey powder with a 100.0% correct overall classification rate of the samples.

The Application of FTIR Spectroscopy and Chemometrics for the Authentication Analysis of Horse Milk

Expensive milk such as horse's milk (HM) may be the target of adulteration by other milk such as goat's milk (GM) and cow's milk (CM). FTIR spectroscopy in combination with chemometrics of linear discriminant analysis (LDA) and multivariate calibrations of partial least square regression (PLSR) and principal component regression (PCR) was used for authentication of HM from GM and CM. Milk was directly subjected to attenuated total reflectance (ATR) spectral measurement at midinfrared regions (4000-650 cm-1). Results showed that LDA could make clear discrimination between HM and HM adulterated with CM and GM without any misclassification observed. PLSR using 2nd derivative spectra at 3200-2800 and 1300-1000 cm-1 provided the best model for the relationship between actual values of GM and FTIR predicted values than PCR. At this condition, R 2 values for calibration and validation models obtained were 0.9995 and 0.9612 with RMSEC and RMSEP values of 0.0093 and 0.0794. PLSR using normal FTIR spectra at 3800-3000 and 1500-1000 cm-1 offered R 2 for the relationship between actual values of CM and FTIR predicted values of >0.99 in calibration and validation models with low errors of RMSEC of 0.0164 and RMSEP of 0.0336 during authentication of HM from CM. Therefore, FTIR spectroscopy in combination with LDA and PLSR is an effective method for authentication of HM from GM and CM.

2D Materials-Based Aptamer Biosensors: Present Status and Way Forward

Current advances in constructing functional nanomaterials and elegantly designed nanostructures have opened up new possibilities for the fabrication of viable field biosensors. Two-dimensional materials (2DMs) have fascinated much attention due to their chemical, optical, physicochemical, and electronic properties. They are ultrathin nanomaterials with unique properties such as high surface-to-volume ratio, surface charge, shape, high anisotropy, and adjustable chemical functionality. 2DMs such as graphene-based 2D materials, Silicate clays, layered double hydroxides (LDHs), MXenes, transition metal dichalcogenides (TMDs), and transition metal oxides (TMOs) offer intensified physicochemical and biological functionality and have proven to be very promising candidates for biological applications and technologies. 2DMs have a multivalent structure that can easily bind to single-stranded DNA/RNA (aptamers) through covalent, non-covalent, hydrogen bond, and π-stacking interactions, whereas aptamers have a small size, excellent chemical stability, and low immunogenicity with high affinity and specificity. This review discussed the potential of various 2D material-based aptasensor for diagnostic applications, e.g., protein detection, environmental monitoring, pathogens detection, etc.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.