Advances of Spectrometric Techniques in Food Analysis and Food Authentication Implemented with Chemometrics

Comprehensive review on the application of omics analysis coupled with Chemometrics in gelatin authentication of food and pharmaceutical products

Gelatin is a protein molecule that can be hydrolyzed from collagen, animal bones, skin and it easily soluble in water. Source animals for gelatin ingredients must be evaluated, as well as their halal status. The omics method towards gelatin authentication in food and pharmaceutical products has several advantages, including high sensitivity and reliable data. Omics investigation employs the process of breaking down substances into small particles, hence enhancing the ability to detect a greater number of compounds. Omics study has the capability to identify substances at the subclass level, which makes it highly suitable for gelatin authentication. Gelatin lipids, metabolites, proteins, and volatile chemicals can be utilized as references to authenticate gelatin. In adopting gelatin authentication, lipidomics, metabolomics, proteomics, and volatilomics must be combined with chemometrics for data interpretation. Chemometrics can convert omics analysis data into easily viewable data. Chemometric approaches capable of presenting omics analysis data for gelatin authentication include PCA, HCA, PLS-DA, PLSR, SIMCA, and FACS. Visually chemometrically explain the differences in gelatin from different animal sources. The combination of omics analysis and chemometrics is a very promising technology for gelatin authentication in food and pharmaceutical products.

An optimised and validated surrogate analyte A-TEEM-PARAFAC-PLS technique for detecting and quantifying the biological oxygen demand in surface water

A 5-day test duration makes BOD5 measurement unsatisfactory and hinders the development of a quick technique. Protein-like fluorescence peaks show a strong correlation between the BOD characteristics and the fluorescence intensities. For identifying and measuring BOD in surface water, a simultaneous absorbance-transmittance and fluorescence excitation-emission matrices (A-TEEM) method combined with PARAFAC (parallel factor) and PLS (partial least squares) analyses was developed using a tyrosine and tryptophan (tyr-trpt) mix as a surrogate analyte for BOD. The use of a surrogate analyte was decided upon due to lack of fluorescent BOD standards. Tyr-trpt mix standard solutions were added to surface water samples to prepare calibration and validation samples. PARAFAC analysis of excitation-emission matrices detected the tyr-trpt mix in surface water. PLS modelling demonstrated significant linearity (R2 = 0.991) between the predicted and measured tyr-trypt mix concentrations, and accuracy and robustness were all acceptable per the ICH Q2 (R2) and ASTM multivariate calibration/validation procedures guidelines. Based on a suitable and workable surrogate analyte method, these results imply that BOD can be detected and quantified using the A-TEEM-PARAFAC-PLS method. Very positive comparability between tyr-trypt mix concentrations was found, suggesting that tyr-trypt mix might eventually take the place of a BOD-based sampling protocol. Overall, this approach offers a novel tool that can be quickly applied in water treatment plant settings and is a step in supporting the trend toward rapid BOD determination in waters. Further studies should demonstrate the wide application of the method using real wastewater samples from various water treatment facilities.

Tracking the Mandorla di Avola Almond Variety by Means of ICP Analysis

The Mandorla di Avola is recognized all over the world as one of the best almond varieties. It is cultivated in a small area inside the provinces of Siracusa and Ragusa (Sicily, southern Italy). It is used in traditional Sicilian cuisine for both salty and sweet foods and of course in artisan pastry, apart from being consumed as a fruit. Due to its extraordinary organoleptic and beneficial features, the Mandorla di Avola is frequently counterfeit with almond varieties of lower quality coming from other countries. While its nutraceutical features have been studied, the possibility of authenticating it with respect to other varieties has not been explored. In this work, we used microelements determined with ICP-OES and ICP-MS as chemical descriptors to distinguish samples of Mandorla di Avola almonds from almonds coming from California and Spain, which are usually employed as substitutes in pastry. Among the different elements determined, Mn and P were found to be the best descriptors for authentication.

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

Future perspectives on aptamer for application in food authentication

Food fraudulence and food contamination are major concerns, particularly among consumers with specific dietary, cultural, lifestyle, and religious requirements. Current food authentication methods have several drawbacks and limitations, necessitating the development of a simpler, more sensitive, and rapid detection approach for food screening analysis, such as an aptamer-based biosensor system. Although the use of aptamer is growing in various fields, aptamer applications for food authentication are still lacking. In this review, we discuss the limitations of existing food authentication technologies and describe the applications of aptamer in food analyses. We also project several potential targets or marker molecules to be targeted in the SELEX process. Finally, this review highlights the drawbacks of current aptamer technologies and outlines the potential route of aptamer selection and applications for successful food authentication. This review provides an overview of the use of aptamer in food research and its potential application as a molecular reporter for rapid detection in food authentication process. Developing databases to store all biochemical profiles of food and applying machine learning algorithms against the biochemical profiles are urged to accelerate the identification of more reliable biomarker molecules as aptamer targets for food authentication.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190-400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky-Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250-400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.