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Ion mobility spectrometry coupled to gas-chromatographic (GC-IMS) is tested regarding their ability to analyze various refined edible vegetable oils, including sunflower seed, rapeseed, sesame, soybean, peanut, corn, camellia, linseed, walnut, coconut, grape seed and extra virgin olive oils. GC-IMS assay displays peak difference of each edible vegetable oil in three-dimensional information at retention time in gas phase and at ion mobility rate in IMS. Moreover, 74 main peak intensities are extracted and imported into Excel for analysis. Then, chemometric methods are employed to establish discriminant models. The results show that based on Kennards-Stone (KS), the prediction accuracy of the soft independent modeling of class analogy (SIMCA) is perfect. Therefore, the GC-IMS system is shown to be an effective method to classification of edible vegetable oils.

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

The volatile organic compounds (VOCs) in different processing stages of dried pork slices (DPS) were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS), which was used to construct the GC-IMS odor fingerprint spectrometry. The VOCs of raw meat, mixed meat, cured meat, semi-finished meat and finished meat were detected and analyzed by GC-IMS. The result shows that the main VOC species in DPS include alcohols and aldehydes, acids, ketones, heterocyclic compounds, aromatic hydrocarbons and esters. Except that the VOCs of mixed meat and cured meat were very similar, the odor component characteristics of DPS in other processing stages were significantly different. GC-IMS technology can effectively distinguish the volatile component differences of preserved meat samples at different processing stages. It is of profound significance to regulate and ensure the consistency of the VOCs in the processing of DPS.

The aim of the present study was to detect adulteration of canola oil with other vegetable oils such as sunflower, soybean, and peanut oils and to build models for predicting the content of adulterant oil in canola oil. In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. The results evaluated by canonical discriminant analysis (CDA) algorithm indicated that the HOG-MPCA-CDA model was feasible to discriminate the canola oil adulterated with other oils and to precisely classify different levels of each adulterant oil. Partial least square analysis (PLS) was used to build prediction models for adulterant oil level in canola oil. The model built by PLS was proven to be effective and precise for predicting adulteration with good regression (R2>0.95) and low errors (RMSE ≤ 3.23).

To investigate the optimum parameters of gas chromatography ion mobility spectrometry (GC-IMS) in determination of flavors and fragrances in tobacco. The effects of reaction parameters such as chromatographic column temperature, incubation temperature, incubation time and injection volume are evaluated with the number of identified characteristic peaks, peak intensity and peak pacing as indexes by an orthogonal L 9 (3 4 ) test. Furthermore, prediction of test results is conducted based on the PLS method. Particle Swarm Optimization (PSO) is used to optimize the parameters. The collection of 30 different flavors and fragrances are tested by GC - IMS which operation parameters are set according to optimal results. The results show that a total of 60 kinds of the volatile organic compounds (VOCs) are identified across all samples, including aldehydes, ketones, alcohols, esters, ethers and so on. Therefore, by means of fingerprints can infer the general composition of fragrance-base or distinguish flavors and fragrances.

In order to explore spectral standardization methods for spectra collected by different NIR spectrometers, to reduce spectral differences, and to realize model sharing among different instruments, the crude protein content of 154 wheat flour samples was measured using one grating and three Fabry-Perot tunable filter NIR spectrometers in wavelength. At the same wavelength range and wavelength interval, three algorithms, namely, direct standardization (DS), piecewise direct standardization (PDS), and simple linear regression direct standardization (SLRDS), were used to standardize spectra collected by different instruments from the same samples. Spectral standardization error rate (SSER), principal component score error rate (PCSER), and other indicators were employed to analyze the spectral differences between the master and the target spectra, and the effect of model sharing was evaluated using parameters including prediction correlation coefficient (Rp), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD). The results show the following: (1) The difference between spectra can be quantitatively evaluated through analyzing SSER and PCSER. (2) After standardization by the three algorithms, the spectral difference between the three target and the master spectrometers is significantly reduced and the prediction effect of the master model is greatly improved. (3) Among the three algorithms, DS algorithm had the smallest error rate in standardizing spectra from three target spectrometers. After standardization by the DS algorithm, the master model had the best effect. Its prediction accuracy was greatly improved compared with that before standardization. (4) The standard model established based on the S450 spectrometer can be applied to the same spectrometer as the N500 spectrometer with the same resolution and different wavelength ranges, so as to achieve model sharing. Therefore, DS, PDS, and SLRDS algorithms can effectively reduce the spectral differences between different instruments and realize the sharing of NIR calibration models for wheat flour crude protein measurement.

Fatty acids synthesized in chloroplast are transported to endoplasmic reticulum (ER) for triacylglycerols (TAGs) resembling. The development of chloroplast also requires lipids trafficking from ER to chloroplast. The membrane contact sites (MCSs) between ER and chloroplast has been demonstrated to be involved for the trafficking of lipids and proteins. Lipids trafficking between ER and chloroplast is often accompanied by lipids interconversion. However, it is rarely known how lipids interconversion happens during their trafficking. We cloned a lipase gene from Brassica napus L., designated as BnCLIP1. Green fluorescence protein (GFP)-tagged BnCLIP1 was shown to locate at the MCSs between ER and chloroplasts in tobacco leaves. Heterogeneous expression of BnCLIP1 in Saccharomyces cerevisiae (pep4) reduced the total amount of fatty acid. Gas chromatography-mass spectrometry (GC-MS) analysis revealed that the truncated BnCLIP1 had a substrate preference for C16:0 lipids in Saccharomyces cerevisiae (pep4). To probe the physiological function of BnCLIP1, two Brassica napus lines with different oil-content were introduced to investigate the transcript patterns of BnCLIP1 during seed development. Intriguingly, the transcript level of BnCLIP1 was found to be immediately up-regulated during the natural seed senescence of both lines; the transcription response of BnCLIP1 in the high oil-content seeds was faster than the lower ones, suggesting a potential role of BnCLIP1 in affecting seed oil synthesis via regulating chloroplast integrity. Further researches showed that chemical disruption of leaf chloroplast also activated the transcription of BnCLIP1. The findings of this study show that BnCLIP1 encodes a lipase, localizes at the MCSs and involves in chloroplast development.

In this study, time-resolved emission fluorescence (TRES) combined with chemometrics was developed and employed for adulteration analysis of camellia oil. TRES was first decomposed by parallel factors analysis (PARAFAC). Next, an artificial neural network (ANN) model was built for the adulteration analysis. A linear range of 5–50%, a limit of detection (LOD) of 3% and root mean square error of prediction ( RMSEP ) values lower than 3% were achieved. Compared with the steady-state measurement, easy access to the information from fluorophores of low concentration was shown to be an intrinsic advantage of the time-resolved measurement; this advantageous characteristic was helpful for optimizing adulteration analysis. It was demonstrated that TRES combined with chemometrics was a simple, rapid and non-intrusive method for adulteration analysis of vegetable oil.

Rice mildew is a crucial problem for safe storage of high-quality grain. A rapid detection method for early warning of rice mildew is in great need by large-volume barns. In this work, gas chromatography-ion mobility spectrometry (GC-IMS) combined with chemometrics was used to detect mildew odor for realizing early prediction of rice mildew occurrence. 21 characteristic substances from volatile organic compounds of rice samples were identified and selected as features to characterize rice quality change in the mildew process. The obtained peaks gallery corresponded to odor substances showed that there were significant changes in composition and concentration of volatile organic compounds during the mildew process. Principal component analysis and k-means clustering algorithm were combined to build a clustering model and the whole mildew process was divided into four periods. These results confirm the potency of GC-IMS as a reliable analytical screening technique, which can be used to quickly identify the degree of rice mildew.

In this paper, a headspace instrument equipped with gas chromatography-ion mobility spectrometry (GC-IMS) was used to classify three kinds of vegetable oils in cooperation with chemometric tools. The procedure contained direct loading of the vegetable oil sample into a vial, headspace generation, and automatic injection of volatile organic components into GC-IMS device. A total of 187 oil samples were detected by GC-IMS, and Otsu’s threshold segmentation and colorized difference methods were adopted to realize automatic peak detection of two-dimensional matrix and comparative visualization for further chemometric pretreatment. Based on the obtained data, principal components analysis showed that 95.77% of sample information could be explained by the first two principal components. Moreover, the oil samples were divided into calibration set (n = 130) and prediction set (n = 57), and the model built by the k-nearest neighbors algorithm showed that the recognition accuracy of calibration set was 100% and the recognition accuracy of prediction set was 98.24%. These results verify that digital image processing methods applied to GC-IMS datasets could preserve chemical information and support qualitative analysis. Thus, GC-IMS technique can be considered a vanguard and reliable tool for recognition of different types of common vegetable oils.