A Near Infrared Spectroscopy (NIRS) and Chemometric Approach to Improve Apple Fruit Quality Management: A Case Study on the Cultivars "Cripps Pink" and "Braeburn"

The potential of near infrared spectroscopy (NIRS) in the wavelength range of 1000–2500 nm for predicting quality parameters such as total soluble solids (TSS), acidity (TA), firmness, and individual sugars (glucose, fructose, sucrose, and xylose) for two cultivars of apples (“Braeburn” and “Cripps Pink”) was studied during the pre- and post-storage periods. Simultaneously, a qualitative investigation on the capability of NIRS to discriminate varieties, harvest dates, storage periods and fruit inhomogeneity was carried out. In order to generate a sample set with high variability within the most relevant apple quality traits, three different harvest time points in combination with five different storage periods were chosen, and the evolution of important quality parameters was followed both with NIRS and wet chemical methods. By applying a principal component analysis (PCA) a differentiation between the two cultivars, freshly harvested vs. long-term stored apples and, notably, between the sun-exposed vs. shaded side of apples could be found. For the determination of quality parameters effective prediction models for titratable acid (TA) and individual sugars such as fructose, glucose and sucrose by using partial least square (PLS) regression have been developed. Our results complement earlier reports, highlighting the versatility of NIRS as a fast, non-invasive method for quantitative and qualitative studies on apples.

Exogenous Application of 5-Aminolevulinic Acid Promotes Coloration and Improves the Quality of Tomato Fruit by Regulating Carotenoid Metabolism

5-Aminolevulinic acid (ALA) plays an important role in plant growth and development. It can also be used to enhance crop resistance to environmental stresses and improve the color and internal quality of fruits. However, there are limited reports regarding the effects of ALA on tomato fruit color and its regulatory mechanisms. Therefore, in this study, the effects of exogenous ALA on the quality and coloration of tomato fruits were examined. Tomato (Solanum lycopersicum "Yuanwei No. 1") fruit surfaces were treated with different concentrations of ALA (0, 100, and 200 mg⋅L-1) on the 24th day after fruit setting (mature green fruit stage), and the content of soluble sugar, titratable acid, soluble protein, vitamin C, and total free amino acids, as well as amino acid components, intermediates of lycopene synthetic and metabolic pathways, and ALA metabolic pathway derivatives were determined during fruit ripening. The relative expression levels of genes involved in lycopene synthesis and metabolism and those involved in ALA metabolism were also analyzed. The results indicated that exogenous ALA (200 mg⋅L-1) increased the contents of soluble sugars, soluble proteins, total free amino acids, and vitamin C as well as 11 kinds of amino acid components in tomato fruits and reduced the content of titratable acids, thus improving the quality of tomato fruits harvested 4 days earlier than those of the control plants. In addition, exogenous ALA markedly improved carotenoid biosynthesis by upregulating the gene expression levels of geranylgeranyl diphosphate synthase, phytoene synthase 1, phytoene desaturase, and lycopene β-cyclase. Furthermore, exogenous ALA inhibited chlorophyll synthesis by downregulating the genes expression levels of Mg-chelatase and protochlorophyllide oxidoreductase. These findings suggest that supplementation with 200 mg⋅L-1 ALA not only enhances the nutritional quality and color of the fruit but also promotes early fruit maturation in tomato.

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

A firm texture of dry onions is important for consumer acceptance. Both the texture and dry matter content decline during storage, influencing the market value of onions. The main goal of this study was to develop predictive models that in future might form the basis for automated sorting of onions for firmness and dry matter content in the industry. Hyperspectral scanning was conducted in reflectance mode for six commercial batches of onions that were monitored three times during storage. Mean spectra from the region of interest were extracted and partial least squares regression (PLSR) models were constructed. Feature wavelengths were identified using variable selection techniques resulting from interval partial least squares and recursive partial least squares analyses. The PLSR model for firmness gave a root mean square error of cross-validation (RMSECV) of 0.84 N, and a root mean square error of prediction (RMSEP) of 0.73 N, with coefficients of determination ( R²) of 0.72 and 0.83, respectively. The RMSECV and RMSEP of the PLSR model for dry matter content were 0.10% and 0.08%, respectively, with a R² of 0.58 and 0.79, respectively. The whole wavelength range and selected wavelengths showed nearly similar results for both dry matter content and firmness. The results obtained from this study clearly reveal that hyperspectral imaging of onion bulbs with selected wavelengths, coupled with chemometric modeling, can be used for the noninvasive determination of the firmness and dry matter content of stored onion bulbs.

Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing

The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.

Overcoming Physiological Bottlenecks of Leaf Vitality and Root Development in Cuttings: A Systemic Perspective

Each year, billions of ornamental young plants are produced worldwide from cuttings that are harvested from stock plants and planted to form adventitious roots. Depending on the plant genotype, the maturation of the cutting, and the particular environment, which is complex and often involves intermediate storage of cuttings under dark conditions and shipping between different climate regions, induced senescence or abscission of leaves and insufficient root development can impair the success of propagation and the quality of generated young plants. Recent findings on the molecular and physiological control of leaf vitality and adventitious root formation are integrated into a systemic perspective on improved physiologically-based control of cutting propagation. The homeostasis and signal transduction of the wound responsive plant hormones ethylene and jasmonic acid, of auxin, cytokinins and strigolactones, and the carbon-nitrogen source-sink balance in cuttings are considered as important processes that are both, highly responsive to environmental inputs and decisive for the development of cuttings. Important modules and bottlenecks of cutting function are identified. Critical environmental inputs at stock plant and cutting level are highlighted and physiological outputs that can be used as quality attributes to monitor the functional capacity of cuttings and as response parameters to optimize the cutting environment are discussed. Facing the great genetic diversity of ornamental crops, a physiologically targeted approach is proposed to define bottleneck-specific plant groups. Components from the field of machine learning may help to mathematically describe the complex environmental response of specific plant species.

Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt-T

Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt-T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location-aware circular convolution (ParC) was introduced, utilizing a ParC-block to replace a portion of the original ConvNeXt-T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC-ConvNeXt-SMU-T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks. PRACTICAL APPLICATION: This study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.