Assessment of laser induced breakdown spectroscopy as a tool for analysis of butter adulteration

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Fish is an essential source of many nutrients necessary for human health. However, the deliberate mislabeling of similar fish fillet types is common in markets to make use of the relatively high price difference. This is a type of explicit food adulteration. In the present work, spectrochemical analysis and chemometric methods are adopted to disclose this type of fish species cheating. Laser-induced breakdown spectroscopy (LIBS) was utilized to differentiate between the fillets of the low-priced tilapia and the expensive Nile perch. Furthermore, the acquired spectroscopic data were analyzed statistically using principal component analysis (PCA) and artificial neural network (ANN) showing good discrimination in the PCA score plot and a 99% classification accuracy rate of the implemented ANN model. The recorded spectra of the two fish indicated that tilapia has a higher fat content than Nile perch, as evidenced by higher CN and C2 bands and an atomic line at 247.8 nm in its spectrum. The obtained results demonstrated the potential of using LIBS as a simple, fast, and cost-effective analytical technique, combined with statistical analysis for the decisive discrimination between fish fillet species.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

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.

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Non-targeted detection of butter adulteration using pointwise UHPLC-ELSD and UHPLC-UV fingerprints with chemometrics

A non-targeted chemometric method was devised to detect possible butter adulteration without prior knowledge of the adulterant and marker compounds. Nine common edible oils including vegetable oils, animal fats and margarines were selected as potential adulterants to build a unified classification model. The samples were analyzed using the high-performance liquid chromatography hyphenated with an evaporative light scattering detector (UHPLC-ELSD) and an ultraviolet detector (UHPLC-UV), with the pointwise chromatograms instead of individual peaks for modelling. Both models achieved over 95% correct classification in external validation at the adulteration levels as low as 5% (w/w). The root mean squared errors of prediction (RMSEP) of the regression model were 0.9865 and 1.9080 for UHPLC-ELSD and UHPLC-UV, respectively. Non-targeted chemometrics analyses based on pointwise chromatographic profiles could be valuable for detecting adulterated butter.

Signal Enhancement Evaluation of Laser-Induced Breakdown Spectroscopy of Extracted Animal Fats Using Principal Component Analysis Approach

In this work, principal component analysis (PCA) was utilized to analyze laser-induced breakdown spectroscopy (LIBS) signals of the extracted chicken fat, lamb fat, beef fat, and lard froze using two different freezing methods. The frozen samples were ablated using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with a wavelength of 1064 nm, 170 mJ pulse energy, and 6 ns pulse duration to produce plasma on target surfaces. The samples were ablated using 30-60 shots of the laser beam at different spots. Stronger LIBS signals from the extracted chicken fat and lamb fat were obtained with liquid nitrogen (LN₂) method. However, LIBS signals obtained from the freezer freezing method were found to be stronger for extracted beef fat and lard. The PCA was then used to visualize the LIBS spectra of extracted animal fats into a score plot. Data points of each extracted animal fat were divided into three groups representing LIBS spectra collected at the early, middle, and end part of the ablation process. The score plot revealed that the data points of the three groups of frozen extracted animal fats using the LN₂ method were more closely clustered than those frozen in the freezer. Good discrimination with 97% of the variance was achieved between the extracted chicken fat, lamb fat, beef fat, and lard using the LN₂ method in the three-dimensional score plot. LIBS signals of the extracted animal fats produced from the LN₂ method were found to be more stable than those from the freezer method.

Emission Spectrochemical Analysis of Soft Samples Including Raw Fish by Employing Laser-Induced Breakdown Spectroscopy with a Subtarget at Low-Pressure Helium Gas

Laser-induced breakdown spectroscopy (LIBS) to detect the light elements such as lithium (Li) and boron (B) and heavy elements such as copper (Cu) and lead (Pb) in raw fish samples is reported in this work. This is made possible by understanding that the soft target absorbs recoil energy and as a result, the ablated atoms gushing from the soft target do not acquire sufficient speed to form a shock wave. In order to overcome this problem, we set a subtarget on the back of the soft target so as to produce the repulsion force by which the gushing speed of the ablated atoms is increased, yielding a sufficiently high plasma temperature or sufficiently large thermal energy needed for the excitation of the ablated atoms. Excellent spectral qualities of various soft samples such as margarine, butter, peanut butter, strawberry jam, raw tuna, raw gindara, and raw salmon are presented. Furthermore, a linear calibration curve with a zero intercept is also obtained for Li, Cu, and Pb. The detection limit of Li, Cu, and Pb is found to be around 0.1 mg/L. This modification of LIBS for soft samples by using a subtarget effect clearly promises a rapid and in situ soft sample analysis since there is practically no sample digestion in the analysis.

High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy

There are many challenges in the determination of elements in complex matrix such as soil, coal and minerals by laser induced breakdown spectroscopy (LIBS) method. Due to the influence of matrix effect, instability of laser plasma and fluctuation of laser parameters, the repeatability and accuracy of quantitative results are always not satisfactory. In order to improve the accuracy, high-energy laser (30mJ-100mJ) with precise control was utilized in many laboratories. In this paper, quantitative analysis of copper in copper concentrate by low-energy (10µJ) LIBS is studied. In order to reduce the influence of matrix effect and other factors, a partial least square regression method based on double genetic algorithm (DGA-PLS) is proposed. The detail operations are as follow: the reference spectral lines are automatically selected by GA as the optimal internal standard for spectral normalization. Then the GA is used to select variables from the normalized spectra for PLS. The results showed that, for univariate model, the coefficient of determination (R²) was improved from 0.6 to 0.97 by the optimal internal standard normalization. Compared with tradition PLS, the root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) of PLS trained by the normalized spectral data decreased from 1.4% and 0.42% to 0.9% and 0.29% respectively. Compared with the normalized PLS, the RMSECV and RMSEP of the DGA-PLS trained by the normalized and feature selected spectral data decreased from 0.9% and 0.29% to 0.26% and 0.21% respectively. The results show that DGA-PLS can significantly reduce matrix effect, improve prediction accuracy and reduce the risk of overfitting in determination of copper in copper concentrate.

Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square-support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.

Prediction of black, immature and sour defective beans in coffee blends by using Laser-Induced Breakdown Spectroscopy

One of the most important factors that interfere negatively in coffee global quality has been blends with defective beans, especially those called Black, Immature and Sour (BIS). The methods based on visual-manual estimation of defective beans have shown their inefficiency in coffee value chain for large-scale analysis. The lack of fast, accurate and robust analytical methods for BIS determination is still a research gap. Laser-Induced Breakdown Spectroscopy (LIBS) is a fast, low-cost and residue-free technique capable of performing multielemental determination and investigating organic composition of samples. In the present work, LIBS together with spectral processing and variable selection were evaluated to fit linear regression models for predicting BIS in blends. Models showed high capacity of prediction with RMSEP smaller than 3.8% and R² higher than 80%. Most importantly, measurements are guided by chemical responses, which make LIBS-based methods less susceptible to the visual indistinguishability that occurs in manual inspections.