Non-Destructive Prediction of Titratable Acidity and Taste Index Properties of Gala Apple Using Combination of Different Hybrids ANN and PLSR-Model Based Spectral Data

Optimization of a Lower Irrigation Limit for Lettuce Based on Comprehensive Evaluation: A Field Experiment

When optimizing irrigation methods, much consideration is given to crop growth indicators while less attention has been paid to soil's gaseous carbon (C) and nitrogen (N) emission indicators. Therefore, adopting an irrigation practice that can reduce emissions while maintaining crop yield and quality is of great interest. Thus, open-field experiments were conducted from September 2020 to January 2022 using a single-factor randomized block design with three replications. The lettuce plants ("Feiqiao Lettuce No.1") were grown using four different irrigation methods established by setting the lower limit of drip irrigation to 75%, 65%, and 55% of soil water content at field capacity corresponding to DR1, DR2, and DR3, respectively. Furrow irrigation (FI) was used as a control. Crop growth indicators and soil gas emissions were observed. Results showed that the mean lettuce yield under DR1 (64,500 kg/ha) was the highest, and it was lower under DR3 and FI. The lettuces under DR3 showed greater concentrations of crude fiber, vitamin C, and soluble sugar, and a greater nitrate concentration. Compared with FI, the DR treatments were more conducive to improving the comprehensive quality of lettuce, including the measured appearance and nutritional quality. Among all the irrigation methods, FI had the maximum cracking rate of lettuce, reaching 25.3%, 24.6%, and 22.7%, respectively, for the three continuous seasons. The stem cracking rates under DR2 were the lowest-only 10.1%, 14.4%, and 8.2%, respectively, which were decreased to nearly half compared with FI. The entropy model detected that the weight coefficient evaluation value of DR2 was the greatest, reaching 0.93, indicating that the DR2 method has the optimal benefits under comprehensive consideration of water saving, yield increase, quality improvement, and emission reduction.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Citrus Huanglongbing detection and semi-quantification of the carbohydrate concentration based on micro-FTIR spectroscopy

Citrus Huanglongbing (HLB) is nowadays one of the most fatal citrus diseases worldwide. Once the citrus tree is infected by the HLB disease, the biochemistry of the phloem region in midribs would change. In order to investigate the carbohydrate changes in phloem region of citrus midrib, the semi-quantification models were established to predict the carbohydrate concentration in it based on Fourier transform infrared microscopy (micro-FTIR) spectroscopy coupled with chemometrics. Healthy, asymptomatic-HLB, symptomatic-HLB, and nutrient-deficient citrus midribs were collected in this study. The results showed that the intensity of the characteristic peak varied with the carbohydrate (starch and soluble sugar) concentration in citrus midrib, especially at the fingerprint regions of 1175-900 cm^-1, 1500-1175 cm^-1, and 1800-1500 cm^-1. Furthermore, semi-quantitative prediction models of starch and soluble sugar were established using the full micro-FTIR spectra and selected characteristic wavebands. The least squares support vector machine regression (LS-SVR) model combined with the random frog (RF) algorithm achieved the best prediction result with the determination coefficient of prediction ([Formula: see text]) of 0.85, the root mean square error of prediction (RMSEP) of 0.36%, residual predictive deviation (RPD) of 2.54, and [Formula: see text] of 0.87, RMSEP of 0.37%, RPD of 2.76, for starch and soluble sugar concentration prediction, respectively. In addition, multi-layer perceptron (MLP) classification models were established to identify HLB disease, achieving the overall classification accuracy of 94% and 87%, based on the full-range spectra and the optimal wavenumbers selected by the random frog (RF) algorithm, respectively. The results demonstrated that micro-FTIR spectroscopy can be a valuable tool for the prediction of carbohydrate concentration in citrus midribs and the detection of HLB disease, which would provide useful guidelines to detect citrus HLB disease.