Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Elemental Profiling for the Detection of Food Mixtures: A Proof of Principle Study on the Detection of Mixed Walnut Origins Using Measured and Calculated Data

Element profiling is a powerful tool for detecting fraud related to claims of geographical origin. However, these methods must be continuously developed, as mixtures of different origins in particular offer great potential for adulteration. This study is a proof of principle to determine whether elemental profiling is suitable for detecting mixtures of the same food but from different origins and whether calculated data from walnut mixtures could help to reduce the measurement burden. The calculated data used in this study were generated based on measurements of authentic, unadulterated samples. Five different classification models and three regression models were applied in five different evaluation approaches to detect adulteration or even distinguish between adulteration levels (10% to 90%). To validate the method, 270 mixtures of walnuts from different origins were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Depending on the evaluation approach, different characteristics were observed in mixtures when comparing the calculated and measured data. Based on the measured data, it was possible to detect admixtures with an accuracy of 100%, even at low levels of adulteration (20%), depending on the country. However, calculated data can only contribute to the detection of adulterated walnut samples in exceptional cases.

Analysis of volatile compounds by GCMS reveals their rice cultivars

Due to the similarity in the grain and difference in the market value among many rice varieties, deliberate mislabeling and adulteration has become a serious problem. To check the authenticity, we aimed to discriminate rice varieties based on their volatile organic compounds (VOCs) composition by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS). The VOC profiles of Wuyoudao 4 from nine sites in Wuchang were compared to 11 rice cultivar from other regions. Multivariate analysis and unsupervised clustering showed an unambiguous distinction between Wuchang rice and non-Wuchang rice. Partial least squares discriminant analysis (PLS-DA) demonstrated a goodness of fit of 0.90 and a goodness of prediction of 0.85. The discriminating ability of volatile compounds is also supported by Random forest analysis. Our data revealed eight biomarkers including 2-acetyl-1-pyrroline (2-AP) that can be used for variation identification. Taken together, the current method can readily distinguish Wuchang rice from other varieties which it holds great potential in checking the authenticity of rice.

Combination of light stable isotopic and elemental signatures in Thai Hom Mali rice with chemometric analysis

This study aims to discriminate the geographical origin of Thai Hom Mali rice in order to protect consumers from mislabeling. Stable isotopic and elemental compositions (δ¹³C, δ¹⁵N, δ¹⁸O, As, Br, K, Mn, Rb, and Zn) of Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain were combined with chemometric analysis, linear discriminant analysis (LDA) and partial least squares-discriminant analysis (PLS-DA). The 9 variables combined with LDA can distinguish Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain with 98.2 % correct classification and 94.6 % cross-validation. The efficiency in using stable isotopic and elemental compositions combined with soft PLS-DA was achieved 100 % in discrimination of Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain. The variables δ¹⁵N, Br, K, and Rb were key parameters to discriminate the geographical origin of Thai Hom Mali rice.

Geographical Origin Determination of Cigar at Different Spatial Scales Based on C and N Metabolites and Mineral Elements Combined with Chemometric Analysis

In this paper, five C and N metabolites and eighteen mineral elements were used to identify the cigar's geographical origin on a country scale (Dominica, Indonesia, and China) and on a prefecture scale (Yuxi, Puer, and Lincang in China). The results show that the best origin traceability method is the combination of C and N metabolites and mineral elements method. Its. Its accuracy of cross-validation can achieve 95% on a country scale and 94% on a prefecture scale. Determination accuracy is ranked as identification by combination > mineral elements > C and N metabolites. For geo-origin determination of cigars, mineral element identification is better than that metabolite identification. The algorithm and factors for origin determination are selected. The results can be used to guide cigar agricultural practices and monitor and regulate the cigar in production and circulation.

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.

Determination and multivariate evaluation of the mineral composition of red jambo (Syzygium malaccense (L.))

This work aimed to evaluate the mineral composition of twelve samples of red jambo (Syzygium malaccensis) collected in 10 cities in the state of Bahia. The samples were digested in a digester block with a reflux system and cold finger, and the analytes were determined by optical emission spectrometry with inductively coupled plasma. The accuracy of the method was confirmed by analyzing NIST 1570a certified reference material (spinach leaves) at a 95% confidence level. The results were evaluated through Principal Component Analysis and Hierarchical Cluster Analysis, which allowed the identification of outliers in the results of the city of Jaguaquara. The analyte concentrations in the samples (mg 100 g ^-1) comprised a range of: Ca (3.0-28.9), Fe (0.035-0.125), K (134.8-197.5), Mg (2.7-19.8), Mn (0.012-0.131), Na (0.5-10.8), P (0.24-13.5), Sr (0.010-0.314), and Zn (0.026-0.129). This demonstrates that the fruit can be indicated as a potential nutritional supplement in human nutrition.

Tracing the Geographical Origin of Thai Hom Mali Rice in Three Contiguous Provinces of Thailand Using Stable Isotopic and Elemental Markers Combined with Multivariate Analysis

Rice is a staple food for more than half of the world’s population. The discrimination of geographical origin of rice has emerged as an important issue to prevent mislabeling and adulteration problems and ensure food quality. Here, the discrimination of Thai Hom Mali rice (THMR), registered as a European Protected Geographical Indication (PGI), was demonstrated. Elemental compositions (Mn, Rb, Co, and Mo) and stable isotope (δ¹⁸O) in the rice were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and elemental analyzer isotope ratio mass spectrometry (EA-IRMS), respectively. The recoveries and precisions of all elements were greater than 98% and lower than 9%, respectively. The analytical precision (±standard deviation) was below ±0.2‰ for δ¹⁸O measurement. Mean of Mn, Rb, Co, Mo, and δ¹⁸O levels was 14.0 mg kg⁻¹, 5.39 mg kg⁻¹, 0.049 mg kg⁻¹, 0.47 mg kg⁻¹, and 25.22‰, respectively. Only five valuable markers combined with radar plots and multivariate analysis, linear discriminant analysis (LDA) could distinguish THMR cultivated from three contiguous provinces with correct classification and cross-validation of 96.4% and 92.9%, respectively. These results offer valuable insight for the sustainable management and regulation of improper labeling regarding geographical origin of rice in Thailand and other countries.

Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures

Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%.