Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis

The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (n = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08-3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (n = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.

Monitoring Fatty Acid and Sterol Profile of Nizip Yaglik Olive Oil Adulterated by Cotton and Sunflower Oil

In this study virgin olive oil obtained from cv. Nizip Yaglik (NY) was adulterated with different proportions (5 and 10%, v/v) of cotton (CO) and sunflower (SO) oils. Fatty acid and sterol profiles of olive oil were analyzed by using gas chromatography (GC). Also, difference of Equivalent Carbon Number 42 values (ΔECN42) of oil samples were determined by using GC and HPLC. Due to results of fatty acids analysis, the percentage of oleic acid was decreased when CO and SO were added. Palmitic acid was increased over the addition of CO, and decreased with the addition of SO. The ΔECN42 values were increased in adulterated oils. These values showed further increase in adulterated oils with SO. Beta-sitosterols decreased to 91.06 and 88.54% when mixed with 5 and 10% SO, respectively. On the other hand, decline was negligible when mixed with CO. According to principal component analyses (PCA), pure NY and adulterated oils were clearly separated in different parts of screen plot according to fatty acids, triacylglycerol (TAGs) and sterol profile. The outcomes of this first investigation provide valuable information for about the differences of fatty acids, ΔECN42 values and sterol compounds between Turkish olive oil from Nizip Yaglik cv. and its adulteration with cotton and sunflower oil. It was observed that fatty acids are not very effective in detecting adulteration of NY oil, but ΔECN values, sterols and Rmar values can be used to detect adulteration of NY olive oil.