Rapid assessment of the authenticity of limequat fruit using the electronic nose and gas chromatography coupled with mass spectrometry

Effects of High-Pressure, Hydrothermal, and Enzyme-Assisted Treatment on the Taste and Flavor Profile of Water-Soluble Ginger (Zingiber officinale) Extract

Ginger, a plant widely consumed worldwide, is used as a spice or to enhance the flavor of foods. In this study, the taste characteristics (gingerol, shogaol, and amino acid) of extracts treated with various solubilizing methods were objectively compared. In addition, an E-nose confirmed the flavor pattern combined with principal component analysis (PCA) between each extract gas chromatogram-tandem mass spectrometry was performed to compare and analyze volatile compounds between extraction methods. As a result, high-pressure enzyme-assisted extraction (HPE) and hydrothermal enzyme-assisted extraction (HWE) treatment effectively improved the extraction yield of ginger and the contents of gingerol and shogaol and removed the bitter taste. In addition, radar charts of both E-nose and PCA provided the distribution of flavor substances in HPE and HWE products of ginger. After enzyme-assisted treatment, a strong fruity and piquant flavor was noted. In conclusion, it is suggested that ginger extract of enzyme-assisted treatment has increased flavor compounds and can be an excellent food material.

A review of fruit juice authenticity assessments: Targeted and untargeted analyses

Fruit juices are becoming more and more popular in the whole world. However, the increasing fruit juice fraud cases are undermining the healthy development of fruit juice industry. Fruit juice authenticity represents an important food quality and safety parameter. Many techniques have been applied in fruit juices authenticity assessment. The purpose of this review is to provide a research overview of the targeted and untargeted analyses of fruit authentication, and a method selection guide for fruit juice authenticity assessment. Targeted markers, such as stable isotopes, phenolics, carbohydrates, organic acids, volatile components, DNAs, amino acids and proteins, as well as carotenoids, will be discussed. And untargeted techniques, including liquid/gas chromatography-mass spectrometer, nuclear magnetic resonance, infrared spectroscopy, inductively-coupled plasma-mass spectrometry/optical emission spectrometer, fluorescence spectra, electronic sensors and others, will be reviewed. The emerging untargeted for novel targeted marker analysis will be also summarized.

Comparison of the Physical and Sensory Properties of Hybrid Citrus Fruit Jaffa® Sweetie in Relation to the Parent Fruits

In the presented study, an overall Jaffa sweetie evaluation was made to find a correlation between Citrus grandis Osbeck × Citrus paradisi Macf. and its parent fruits’ (Citrus grandis Osbeck, Citrus paradisi Macf.) properties. Based on the sensory analysis, it was found that the taste and aroma of the new hybrid fruit are close to pummelo. By the use of chromatographic analysis, the selected monoterpenes present in the fruits were quantified. α-terpineol was typed as the main monoterpene compound in the headspace of sweetie and grapefruit, with the concentrations: 20.96 and 87.9 μg/g, respectively. In turn, γ-terpinene was chosen as the most important monoterpene determining the flavor of sweetie fruit. Based on two-dimensional gas chromatography (GC × GC-TOF-MS) and principal component analysis (PCA) of the data, several volatile compounds were associated with analyzed fruits’ aroma. Jaffa Sweetie is the hybrid fruit with sensory properties similar to pummelo with a higher content of monoterpenes, which improves its health benefits compared to the parent fruit. The research presents an instrumental method for assessing the aroma properties of the fruit as a reference method for sensory analysis, commonly used in the industry.

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange

The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the ‘Aiyuan 38’ orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange’s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.