Identification of Aroma-active Compounds in Milk by GC×GC-O-TOF-MS Combined with Check-all-that-apply.

Understanding consumers' sensory preferences for dairy products is essential. This study employed sensory analysis and instrumental techniques to analyze the flavor of pasteurized milk and ultra-high temperature (UHT) milk. There were 6 milk samples with similar fat content (4.0-4.6 g/100mL) and protein content (3.2-3.8 g/100mL). Sensory data from consumer tests was collected using CATA (n = 100) and 9-point hedonic preferences. Research showed that Chinese consumers could distinguish the flavor of the 2 types of milk, and UHT milk showed a higher preference score, which may be due to the more pronounced milky flavor and sweet taste of UHT milk. A total of 48 aroma-active compounds were sniffed through GC × GC-O-TOF-MS, among which 11 were determined as key aroma-active compounds. Correlation analysis showed that milky odor, sweetness, and aftertaste-milky were positively correlated with γ-dodecalactone and γ-nonanolactone. Cooked and oxidized taste were positively correlated with 1-octen-3-ol and E-2-octenal. This study is important for developing Chinese dairy products and exporting dairy products to China by multinational companies.

Evaluation of the freshness of rainbow trout (Oncorhynchus mykiss) fillets by the NIR, E-nose and SPME-GC-MS.

A comparison study on the freshness of rainbow trout (Oncorhynchus mykiss) fillets in the course of their sale was performed using near-infrared spectroscopy (NIRS), solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), and the electronic nose (E-nose) technique. Quantitative analysis of the volatile salt nitrogen (TVB-N) of rainbow trout fillets with different freshness using NIR combined with the partial least squares (PLS) method revealed that the predicted values of TVB-N of the samples were significantly correlated with the true values (P < 0.01). SPME-GC-MS combined with E-nose analysis demonstrated that there were significant differences in the volatile flavor components of rainbow trout fillets at different freshness, and E-nose combined with principal component analysis (PCA) and linear discriminant analysis (LDA) could achieve rapid and non-destructive freshness ranking of rainbow trout fillets based on volatile flavor characteristics. Consequently, the NIRS and E-nose non-destructive testing techniques are capable of acting as rapid screening tools for detecting the freshness of rainbow trout fillets during their sale.

A novel electronic nose for the detection and classification of pesticide residue on apples.

Excessive pesticide residues are a serious problem faced by food regulatory authorities, suppliers, and consumers. To assist with this challenge, this work aimed to develop a method of detecting and classifying pesticide residue on fruit samples using an electronic nose, through the application of three different data-recognition algorithms. The apple samples carried various concentrations of two known pesticides, namely cypermethrin and chlorpyrifos. Data collection was performed using a PEN3 electronic nose equipped with 10 metal oxide semiconductor (MOS) sensors. In order to classify and analyze these pesticide residues on the apple samples, principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) results were combined with sensor output responses to realize MOS sensor array data visualization. The results indicated that all three data-recognition algorithms accurately identified the pesticide residues in the apple samples, with the PCA algorithm exhibiting the best classification and discrimination ability. Consequently, this work has shown that the MOS electronic nose, in combination with data-recognition algorithms, can provide support for the rapid and non-destructive identification of pesticide residues in fruits and can provide an effective tool for the detection of pesticide residues in agricultural products.