Assessment of the optical properties of peaches with fungal infection using spatially-resolved diffuse reflectance technique and their relationships with tissue structural and biochemical properties

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.

Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future.

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.

Assessing and detection of multiple bruises in peaches based on structured hyperspectral imaging

This study aimed to detect various types of postharvest damages in peaches based on structured hyperspectral imaging (S-HSI), including impact, falling, and compression damage, which can lead to bruising. The research involved three different spatial frequencies (60, 100, and 150 m-1) and used a 2π/3 phase shift interval to capture S-HSI images. These images were then processed using a mathematical demodulated model to create high-resolution image cubes that included both image and spectral information from the S-HSI data. Artificial neural network and principal component analysis were applied to develop bruise detection models using S-HSI spectra, which showed better discriminating effects compared with the ordinary hyperspectral spectra. The best performing discriminating models for healthy and three kinds of bruised samples were developed using the spectra of spatial frequency with 100 + 150 m-1, respectively. This study demonstrated the potential of S-HSI as an effective optical technique for bruise detection of peach.

Insights into the Isolation, Identification, and Biological Characterization Analysis of and Novel Control Strategies for Diaporthe passiflorae in Postharvest Passion Fruit

Postharvest diseases seriously restrict developments in the passion fruit industry. In this study, we aimed to identify the postharvest pathogen affecting passion fruit, investigate its pathogenicity, and explore relevant control methods. The pathogen was isolated from rotting passion fruit and identified using morphological characteristics, ITS sequences, and phylogenetic tree analyses. Additionally, preliminary studies were conducted to assess the biological characteristics of the pathogen and evaluate the efficacy of various treatments for disease control. The fungus on the passion fruit called B4 was identified as Diaporthe passiflorae. Optimal conditions for mycelial growth were observed at 25-30 °C and pH 5-6, with starch as the carbon source and peptone as the nitrogen source. Infection by D. passiflorae accelerated fruit decay, reduced the h° value of the peel, and increased the peel cell membrane permeability when compared to the control. Notably, treatments with appropriate concentrations of ɛ-poly-l-lysine, salicylic acid, and melatonin showed inhibitory effects on the pathogen's growth in vitro and may thus be potential postharvest treatments for controlling brown rot caused by D. passiflorae in passion fruit. The results provide a scientific basis for the development of strategies to control postharvest decay and extend the storage period of passion fruit.

Applications of optical property measurement for quality evaluation of agri-food products: a review

Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.

Trichoderma longibrachiatum Inoculation Improves Drought Resistance and Growth of Pinus massoniana Seedlings through Regulating Physiological Responses and Soil Microbial Community

Drought stress poses a serious threat to Pinus massoniana seedling growth in southern China. Trichoderma species, as beneficial microorganisms, have been widely used in agriculture to enhance plant growth and drought tolerance, but the interaction mechanisms remain unclear. To investigate the effect of drought-resistant Trichoderma longibrachiatum inoculation on P. massoniana growth under drought stress, the plant physiological indicators and rhizosphere microbiome diversity were measured to identify Trichoderma-activated mechanisms. Trichoderma longibrachiatum inoculation significantly promoted P. massoniana growth under drought treatment, and enhanced nitrogen, phosphorus, and potassium absorption compared with those of non-inoculated seedlings. Trichoderma longibrachiatum treatment alleviated the damage to cell membranes and needle tissue structure, and significantly increased antioxidant enzyme activities, osmotic substance contents, and photosynthesis in P. massoniana in response to drought stress. Soil nutrient contents, activities of sucrase, phosphatase, and urease as well as the relative abundances of the dominant genera Burkholderia, Rhodanobacter, and Trichoderma were elevated in the rhizosphere soil of P. massoniana inoculated with T. longibrachiatum under drought stress. A network analysis showed that certain crucial dominant taxa driven by T. longibrachiatum inoculation, including Penicillium, Trichoderma, Simplicillium, Saitozyma, Burkholderia, Bradyrhizobium, Sinomonas, and Mycobacterium, had more correlations with other microorganisms in the soil. Trichoderma longibrachiatum enhanced P. massoniana seedling growth under drought stress by regulating physiological responses and soil microbial community.

Assessment of the optical properties with physicochemical properties and cell wall polysaccharides of 'Korla' pear flesh during Alternaria alternata-induced disease development

Cell wall polysaccharides and physicochemical properties are the major quality characteristics of fruit, but they are significantly affected by the postharvest disease. In this study, the influence of Alternaria alternata-induced disease on the contents of cell wall polysaccharides and physicochemical properties in 'Korla' pear flesh during storage, as well as their relationships of the optical absorption (μ_a) and reduced scattering (μ_s') were explored. The infected pear had lower individual sugars, covalent-soluble pectin, cellulose and hemicellulose contents than the healthy ones. The successive decreases of μ_a and increases of μ_s' in pears were observed while the process of pathogen infection. Path-coefficient analysis indicated the ionic-soluble pectin was the main reason responsible for the change of μ_s' in infected pear at 675 nm and 980 nm. This study indicated the optical properties have the possibility to present the physicochemical characteristics and cell wall polysaccharides of pears during postharvest pathogen infection.

Detection of Alternaria alternata infection in winter jujubes based on optical properties and their correlation with internal quality parameters during storage

In this work, a single integrating sphere system was applied to characterize the optical absorption (μ_a) and reduced scattering (μ_s') properties (550 - 1050 nm) in winter jujube flesh infected by Alternaria alternata during storage at 4 and 20 °C, respectively. Meanwhile, physical (L*, a*, weight loss) and biochemical characteristics (soluble solids content, titratable acids, chlorophyll, total phenolic, and ascorbic acid) of winter jujubes were measured. Among them, chlorophyll, weight loss and ascorbic acid were highly correlated with μ_a at 680 nm, 690 nm, while chlorophyll and a* had the best correlations with μ_s' at 700 - 920 nm. These optimal optical properties were proved efficiently contributed to the disease detection of winter jujubes after 12 days at 4 °C and 3 days at 20 °C during storage, with satisfactory discrimination accuracies (acc > 93.75 %). Consequently, optical properties in Vis-NIR region were available to detect the postharvest disease in winter jujubes.

Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease

We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases.

Analysis of Light Penetration Depth in Apple Tissues by Depth-Resolved Spatial-Frequency Domain Imaging

Spatial-frequency domain imaging (SFDI) has been developed as an emerging modality for detecting early-stage bruises of fruits, such as apples, due to its unique advantage of a depth-resolved imaging feature. This paper presents theoretical and experimental analyses to determine the light penetration depth in apple tissues under spatially modulated illumination. Simulation and practical experiments were then carried out to explore the maximum light penetration depths in 'Golden Delicious' apples. Then, apple experiments for early-stage bruise detection using the estimated reduced scattering coefficient mapping were conducted to validate the results of light penetration depths. The results showed that the simulations produced comparable or a little larger light penetration depth in apple tissues (~2.2 mm) than the practical experiment (~1.8 mm or ~2.3 mm). Apple peel further decreased the light penetration depth due to the high absorption properties of pigment contents. Apple bruises located beneath the surface peel with the depth of about 0-1.2 mm could be effectively detected by the SFDI technique. This study, to our knowledge, made the first effort to investigate the light penetration depth in apple tissues by SFDI, which would provide useful information for enhanced detection of early-stage apple bruising by selecting the appropriate spatial frequency.

Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.

Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review

Damage occurs easily and is difficult to find inside fruits and vegetables during transportation or storage, which not only brings losses to fruit and vegetable distributors, but also reduces the satisfaction of consumers. Spatially resolved spectroscopy (SRS) is able to detect the quality attributes of fruits and vegetables at different depths, which is of great significance to the quality classification and defect detection of horticultural products. This paper is aimed at reviewing the applications of spatially resolved spectroscopy for measuring the quality attributes of fruits and vegetables in detail. The principle of light transfer in biological tissues, diffusion approximation theory and methodologies are introduced, and different configuration designs for spatially resolved spectroscopy are compared and analyzed. Besides, spatially resolved spectroscopy applications based on two aspects for assessing the quality of fruits and vegetables are summarized. Finally, the problems encountered in previous studies are discussed, and future development trends are presented. It can be concluded that spatially resolved spectroscopy demonstrates great application potential in the field of fruit and vegetable quality attribute evaluation. However, due to the limitation of equipment configurations and data processing speed, the application of spatially resolved spectroscopy in real-time online detection is still a challenge.

Evaluation of the Changes in Optical Properties of Peaches with Different Maturity Levels during Bruising

The main objective was to measure the optical coefficients of peaches after bruising at different maturity levels and detect bruises. A spatially resolved method was used to acquire absorption coefficient (μ_a) and the reduced scattering coefficient (µ_s') spectra from 550 to 1000 nm, and a total of 12 groups (3 maturity levels * 4 detection times) were used to assess changes in µ_a and µ_s' resulting from bruising. Maturation and bruising both caused a decrease in µ_s' and an increase in µ_a, and the optical properties of immature peaches changed more after bruising than the optical properties of ripe peaches. Four hours after bruising, the optical properties of most samples were significantly different from those of intact peaches (p < 0.05), and the optical properties showed damage to tissue earlier than the appearance symptoms observed with the naked eye. The classification results of the Support Vector Machine model for bruised peaches showed that μ_a had the best classification accuracy compared to μ_s' and their combinations (µ_a × µ_s', µ_eff). Overall, based on μ_a, the average detection accuracies for peaches after bruising of 0 h, 4 h, and 24 h were increased.

Measurement of Early Disease Blueberries Based on Vis/NIR Hyperspectral Imaging System

Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400–1000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400–1000 nm and effective spectral range of 685–1000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.