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Onion ( Allium cepa L.) is one of the most important bulbous vegetable used in global kitchen. Although it has various important traits but among them the high total soluble solid content (HTSS) of white onion is highly preferred by the industries for processing purpose. Looking into the broader use of the white onion; we utilized a total of 23 polymorphic simple sequence repeats (SSRs) and intron length polymorphic markers (ILPs) to characterize thirty five white onion genotypes for HTSS. Further, we also correlated these DNA fingerprint data with the TSS for identification of HTSS and LTSS lines. The total average numbers of alleles for SSR and ILP locus, heterozygosity (He) and polymorphism information content were found to be 2.4, 0.35 and 0.29 respectively. The UPGMA dendrogram revealed two distinct clusters of genotypes. Based on the TSS and DNA genotype data, our study revealed that Bhima-shweta (LTSS-12.09%, sub-cluster IAa) & WHT-12L-HT-15-Reject-M-7(HTSS- 18.02%, cluster II) are more diverse than the others. Other white onion lines including WHTB-7G-GT-15-SC-M-7 small bulb (HTSS 18.80%), WHT-2B-GT-18-SC-M-7 (HTSS 18.51%), WHTS-4D-GT-18-MC-M-7 (HTSS 18.49%), WHTB-3C-GT-18-MC-M-7 (HTSS 18.27%) and WHTS-11K-Pickle-SC-M-7 (HTSS 17.68%) were identified as superior HTSS lines. These identified diverse HTSS and LTSS lines could be useful for the mapping of HTSS coding genes for the acceleration of molecular breeding of onion through the marker assisted selection (MAS) which could be used by the industries for the larger scale processing of the white onion products required by the global community.

The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

A Tungsten-Halogen (TH) lamp is the most popular light source in NIR spectroscopy and hyperspectral imaging, which requires a warm-up to reach very high temperatures of up to 250 °C and take a long time for radiation stabilization. Consequently, it has a large enough volume to enable heat dissipation to prevent the thermal runaway of the electric circuit and turn out its power efficiency very low. These are major barriers for miniaturizing spectral systems and hyperspectral imaging devices. However, TH lamps can be replaced by pc-NIR LEDs in order to avoid high temperature and large volume. We compared the spectral emission of the available commercial pc-NIR LEDs under the same condition. As a replacement for the TH lamp, the VIS + NIR LED module was developed to combine a warm-white LED and pc-NIR LEDs. In order to feature out the availability of the VIS + NIR LED module against the TH lamp, they were used as the light source for evaluating the Soluble Solid Content (SSC) of an apple through VIS-NIR spectroscopy. The results show a remarkable feasibility in the performance of the partial least square (PLS) model using the VIS + NIR LED module; during PLS calibration, the correlation coefficient (R) values are 0.664 and 0.701, and the Mean Square Error (MSE) values are 0.681 and 0.602 for the TH lamp and VIS + NIR LED module, respectively. In VIS-NIR spectroscopy, this study indicates that the TH lamp could be replaceable with a warm-white LED and pc-NIR LEDs.

The experiment was carried out to evaluate the quality of anhydrous milk fat (AMF) of cows and buffaloes supplemented with flaxseed oil (FO), soybean oil (SO), or their mixture (FSO). Lactating crossbred cows and buffaloes were fed with control diet or with one of three supplements: 2% FO, 2% SO, and 2% FSO according to a double 4 x 4 Latin Square Design. The diets with FO, SO, or FSO reduced saturated FA, mainly C4:0, C14:0 and C16:0, while increased the unsaturated FA C18:1 and C18:2 in milk from cows and buffaloes. Cholesterol content decreased in cow's AMF while increased in buffalo's AMF when a diet supplemented with FO, SO, or FSO. The diet with SO or FSO increased the content of vitamin E in AMF obtained from cows (25.06 and 17.89 mg 100 g-1) and buffaloes (28.48 and 30.32 mg 100 g-1) compared with the control diet (11.02 and 15.68 mg 100 g-1), respectively, which correlated positively with scavenging activity for DPPH• (r2 = 0.66) and ABTS• (r2 = 0.67) radicals. Solid fat content (SFC) was high for cow’s AMF, with 58.12-60.37% at 5°C compared to that of buffalo's AMF, with 52.37-56.98%, but was low for cow's AMF at >15°C. Finally, supplementing a diet with vegetable oils, particularly SO, improves the quality of AMF; increases USFA/SFA ratio, vitamin E content, and antioxidant activities

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

Spray drying is a very popular method for microalgal biomass drying; however, systematic research on the response of the biochemical composition during the process of spray drying has not been addressed thus far. This study investigated the influence of the inlet temperature and the initial solid content on the biochemical composition of spray-dried Scenedesmus acuminatus biomass. The fatty acid composition and contents of CHNS, lipids, carbohydrates, protein, starch, and pigments were analyzed to characterize the quality and bioactivity of the dried product. The results showed that the moisture content of the dried microalgal powder decreased with increasing inlet temperature and initial solid content, and the lowest moisture content of 2.37%, with a higher drying yield of 84%, was achieved at an optimized inlet temperature of 220 °C and an initial solid content of 16%. The biochemical compositions of CHNS, total lipids, carbohydrates, protein, starch, and fatty acids in the spray-dried biomass were similar to those in the freeze-dried biomass and were barely altered throughout the spray drying process. The pigment partially degraded as the inlet temperature increased; however, this degradation could be alleviated by increasing the initial solid content of the microalgal suspension because cell aggregates provided protection. Thermogravimetric analysis (TGA) further confirmed that spray drying did not affect the quality of proteins, lipids, or carbohydrates, suggesting that the spray drying technique could be applied to S. acuminatus for the production of both biofuel and nutritional supplements. These results may serve as a reference for the selection of the drying method, the utilization of the nutritional components in S. acuminatus, and the selection of biochemical parameters for spray drying performance evaluation.

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient ( R 2 ) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

Compositional thermodynamic phase separation is investigated for industrial-grade vegetable oils with complex compositions. Solid–liquid equilibria have been calculated by utilizing the Margules 2-suffix activity-coefficient model in combination with minimization of the Gibb’s free energy of the system. On the basis of quasi-equilibrium solid-fat content (SFC) measurements, a new approach to the estimation of the interaction parameters, needed for the activity-coefficient model, has been developed. The parameters are fitted by matching the SFC of two oils at various degrees of dilution and isothermal temperatures. Subsequently, the parameters are successfully validated against three oils, rich in asymmetric and symmetric triacylglycerols (TAG), respectively. The new approach developed is shown to be very flexible, allowing incorporation of additional TAG and polymorphic states. It thereby provides a simple way to dealing with multi-component, multi-phase TAG mixtures without having the required binary interaction parameters at hand a priori. This ultimately provides a powerful, predictive tool which may serve as a starting point for laboratory screening and creation of tailor-made products because many different oil mixtures can be evaluated quickly with respect to specific properties, prior to more time-consuming experimental evaluation.

•High-gravity technology leads to significant reduction of the ethanol recovery cost.•High-gravity leads to challenges in mixing and mass transfer.•Process design is a powerful tool to face the challenges in high gravity processes.•Challenges at high gravity call for the development of robust microorganisms. In brewing and ethanol-based biofuel industries, high-gravity fermentation produces 10–15% (v/v) ethanol, resulting in improved overall productivity, reduced capital cost, and reduced energy input compared to processing at normal gravity. High-gravity technology ensures a successful implementation of cellulose to ethanol conversion as a cost-competitive process. Implementation of such technologies is possible if all process steps can be performed at high biomass concentrations. This review focuses on challenges and technological efforts in processing at high-gravity conditions and how these conditions influence the physiology and metabolism of fermenting microorganisms, the action of enzymes, and other process-related factors. Lignocellulosic materials add challenges compared to implemented processes due to high inhibitors content and the physical properties of these materials at high gravity.