Qualitative Identification Research Articles

Preparation of a multifunctional and ultrasensitive Au@AgNPs-Van/PDMS film SERS substrate for rapid detection of foodborne pathogens in beef

We present a novel and super-sensitive vancomycin (Van) modified Au@AgNP self-assembly with polydimethylsiloxane (PDMS) (Au@AgNPs/Van-PDMS) film surface-enhanced Raman scattering (SERS) substrate for identification of foodborne pathogens in beef, without the need for in vitro bacterial culture. The results illustrated that the Au@AgNPs/Van-PDMS film exhibited high reproducibility and Raman enhancement effect (1.09 × 105). Clostridium perfringens, Bacillus subtilis and Staphylococcus aureus isolated from beef captured by Au@AgNPs/Van-PDMS film exhibited high reproducibility and significant differences. Principal component analysis score plots distinguished classes of foodborne pathogens, and the qualitative identification of linear discriminant analysis was correct at 100%. The detection limit of S. aureus in beef was as low as 3 CFU/mL. This method could provide an effective means and technical support for realizing the application of SERS technology for quick and highly sensitive detection of foodborne pathogens in complex environments.

OPTIMIZED ECOLOGICAL NETWORK APPROACH OF HIGHLY URBANIZED CITIES: THE CASE OF ADANA CITY

Abstract. One of the most significant challenges in urban areas, where the process of rapid urban expansion takes place, is the loss of agricultural lands and natural habitats. The conversion of these areas into residential and commercial zones leads to a decline in urban biodiversity and the progressive loss of vital habitat areas. Analyzing habitat connectivity and conducting landscape measurements provide valuable insights for the development of land use and management strategies, enhancing our understanding of the spatial structure of the landscape, and directing conservation efforts. Incorporating measures such as green corridors and landscape connection networks into urban planning management becomes crucial in order to mitigate the adverse effects of habitat fragmentation and enhance ecosystem resilience within cities. Remote sensing techniques offer opportunities to create habitat connectivity models that enable the quantitative and qualitative identification of fragmented habitat patches. These models serve as tools to evaluate the effectiveness of conservation measures and monitor the potential impacts of future land use changes on habitat networks. Within this context, an optimized approach to habitat connectivity is presented, aiming to contribute to landscape planning and ecological-based studies in a city with undergoing rapid urbanization like Adana. By identifying degraded areas and introducing new habitat patches, a significant improvement in the connectivity of the habitat network has been observed. The findings indicated that the addition of new habitat patches to degraded areas can substantially enhance the city's overall habitat connectivity.

Detection of the Early Fungal Infection of Citrus by Fourier Transform Near-Infrared Spectra

Early fungal infection of citrus is one of the common diseases found during the storage period of citrus, and fungus that infects citrus will spread to the entire batch of citrus as the degree of infection deepens, causing enormous economic losses. Therefore, early detection of fungal infection of citrus is fundamental. The purpose of this study is to explore the qualitative identification of early fungal infections in citrus by using Fourier transform near-infrared (FT-NIR) combined with a variety of chemometric methods. First, discrete wavelet transform (DWT) is used to filter the noise of the spectral signal, then combined with a PLS-DA model, that helps discriminate healthy from infected Citrus. Subsequently, four different feature variable selection methods were introduced, Then, the linear discriminant analysis (LDA) and support vector machine (SVM) two classifiers were combined to establish a qualitative model for the degree of fungal infection. The modeling results show that the SVM modeling effect is better than LDA, and the DWT-CARS-SVM based on the RBF kernel function has the best result, the accuracy rates of the training set and test set are 100% and 97%. The results indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.

Phytochemical profile of petals from black Dahlia pinnata by flow injection analysis-electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry.

Dahlia pinnata Cav. is a flower native to Mexico that has many applications; in particular, its petals have been used for ornamental, food, and medicinal purposes, for example to treat skin rashes and skin cracks. It has been reported that the medicinal properties of plants are generally related to the phytochemical constituents they possess. However, there are few studies on black D. pinnata. The present study was aimed at qualitatively and quantitatively determining the phytochemical profile of petals from black D. pinnata. Phytochemicals from Dahlia petals were extracted by consecutive maceration (hexane, dichloromethane, and methanol); then, the extracts were analyzed through colorimetric assays and UV-Vis spectroscopy for qualitative identification and quantification of phytochemical compounds, respectively. The methanolic extract was analyzed by flow injection analysis-electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (FIA-ESI-FTICR-MS) in negative and positive mode. Quantitative phytochemical profiling of the methanolic extract by UV-Vis spectroscopy indicated high contents of phenolic compounds (34.35 ±3.59 mg EQ/g plant) and sugars (23.91 ±1.99 mg EQ/g plant), while the qualitative profiling by FIA-ESI-FTICR-MS allowed the tentative identification of several flavonoids and phenolic acids. Kaempferol-3-rutinoside, pelargonidin-3-(6″-malonylglucoside)-5-glucoside, rutin, kaempferol-3-(2″,3″-diacetyl-4″-p-coumaroylrhamnoside), and myricetin-3-(2‴-galloylrhamnoside) were the main compounds detected. The results expand our knowledge of the phytochemical constituents of petals from black D. pinnata.

Rapid Characterization of Male and Female Taxus chinensis by Near-Infrared (NIR) Spectroscopy and High-Performance Liquid Chromatography (HPLC) with Orthogonal Partial Least Squares—Discriminant Analysis (OPLS-DA)

Taxus chinensis var. mairei (T. chinensis) is a dioecious plant that is important in traditional Chinese medicine. However, information about male and female T. chinensis is absent. To establish a simple, rapid, and reliable method for the qualitative identification of male and female T. chinensis, near-infrared spectroscopy (NIR) and high-performance liquid chromatography (HPLC) with the orthogonal partial least-squares discriminate analysis (OPLS-DA) mode was performed. NIR and discriminant analysis (DA) were employed for the first time to distinguish male and female T. chinensis. Meanwhile, HPLC also was optimized to determine the contents of the bioactive ingredients 10-deacetyltaxol (10-DAT) and paclitaxel of T. chinensis. Herein, the results of the validation showed that the DA model using NIR had a correct differentiation of nearly 100% for male and female T. chinensis. Moreover, the HPLC results indicated that the content of paclitaxel and 10-DAT was higher in female plants than in males. Additionally, based on the HPLC content results of male and female T. chinensis, the OPLS-DA successfully distinguished male and female T. chinensis. Thus, combining HPLC and OPLS-DA to initially differentiate between males and females allowed further quantitative and qualitative analysis of male and female T. chinensis. This method provides an approach to the identification and quality control of traditional Chinese medicine.

Fluorescence quenching detection of p-nitrophenol in river water using functional perovskite quantum dots

p-Nitrophenol (p-NP) is a chemical compound that produces pesticides and pharmaceuticals. Improper handling can lead to water pollution and pose risks to the environment. Considerable attention has been given to the research and development of methods for detecting water pollutants to achieve Sustainable Development Goal 6. This study used highly fluorescent perovskite quantum dots (PVSK QDs) to detect p-NP in water. To enhance the stability of the PVSK QDs, we performed surface modification using 3-triethoxysilylpropylamine, resulting in functionalized amine-PVSK QDs. This enables the amine-PVSK QDs to maintain a high fluorescent quantum yield of 88.8% after 30 days of storage in ethanol. The detection of p-NP in water is achieved through the fluorescence quenching resulting from the reaction between the amino groups on the amine-PVSK QDs and p-NP. The detection limit for p-NP was 160 nM, lower than the US EPA permissible concentration limit for potable water. The amine-PVSK QDs detected p-NP in two river water samples with a relative standard deviation of 0.31–2.49% range. These findings demonstrate that amine-PVSK QDs can serve as a fast and sensitive fluorescent detection platform for the qualitative and quantitative identification of aqueous p-NP, addressing environmental sustainability issues.

A multi-analyte screening method for the rapid detection of illicit adulterants in dietary supplements using a portable SERS analyzer

The popularity and number of dietary supplements on the health market have experienced an unprecedented boost in recent years. Simultaneously, their increased use has been accompanied by an increase in acute intoxication cases linked to the adulteration of these products with illicit and undeclared substances. In this study, a SERS-based screening methodology was developed to rapidly detect illegally added pharmaceutically active substances to dietary supplements. A portable analyzer and silver printed-SERS substrates were used to enhance the signal, requiring less than 20 min of sample preparation prior to the analysis. The method was successful in the qualitative identification of eleven out of twenty-three illicit adulterants in the dietary supplements; it could detect the target compounds at realistic adulteration levels (0.1–5.0% w/w), demonstrating the potential of SERS-based methodologies for forensic rapid screening applications. The developed method is quick, easy to use, requires no skilled technicians and little sample preparation, and allows in-situ analyses. For these reasons, it is suitable for quick screening to be performed by inspectors at customs. Moreover, the low specificity of spectroscopic methods, to which SERS belongs, would benefit the detection of newly synthesized analogues of the target adulterants, which would otherwise be more difficult using common mass spectrometry methods in absence of reference standards.

IDENTIFICATION AND QUANTITATION OF FEXOFENADINE HYDROCHLORIDE IN MODEL ORAL DROPS

Relevance. Fexofenadine hydrochloride is a representative of the III generation of histamine H1 receptor blockers, therefore, it is not characterized by the disadvantages of antihistamines, such as: suppression of the central nervous system, cardiotoxic effect, headache, impaired coordination of movements, photosensitivity. Analysis of fexofenadine hydrochloride was performed using UV spectroscopy. The purpose of the study – development of a new analytical method for the qualitative and quantitative identification of fixofenadine in drops for in-ternal use. Material and methods. The study used: fexofenadine, sodium hydroxide, water for injection, UV spectrophotometer type SF-104. Results and conclusions. A method for the qualitative and quantitative determination of fexofenadine hydrochloride in model drops for internal use by the SPS method has been proposed. This method meets the requirements of OFS 1.1.0012.15 Validation of analytical methods, in terms of indica-tors: linearity and analytical area, correctness, precision.

Multiscale Fine Characterization of a Coal Pore–Fracture System Based on SEM, CT, and NMR in the Jingbian Block, Ordos Basin

To achieve an accurate and comprehensive characterization of the multiscale pore–fracture characteristics of Permian coal in the Jingbian Block, Ordos Basin, a combination of scanning electron microscopy (SEM), X-ray computed tomography (CT), and nuclear magnetic resonance (NMR) techniques was utilized. With these experiments, the mineral composition, pore size distribution (PSD), porosity, and connectivity of pores in coal samples were characterized through qualitative and quantitative methods. The results show that the SEM experiments enabled qualitative identification of pores and mineral types. The coal samples primarily contained gas pores, cell pores, intercrystalline pores, and moldic pores, and clay minerals were the predominant fracture fillings. The 3D reconstruction of the CT experiments shows that the pores and fractures generally expand horizontally, while the minerals show obvious bedding expansion characteristics. Moreover, the estimation of full-size porosity in coal samples can be achieved by combining CT and NMR experiments. The full-size porosity of samples G11-5-1, G11-5-6, G11-5-9, and G11-5-11 was 8.93%, 9.11%, 10.45%, and 11.63%, respectively. The connectivity differences are primarily determined by the throat development degree and the connected pore–fracture count. Samples with more connected pores and larger throat radii exhibit excellent connectivity.

Chemical Sensors using Single-Molecule Electrical Measurements.

Driven by the digitization and informatization of contemporary society, electrical sensors are developing toward minimal structure, intelligent function, and high detection resolution. Single-molecule electrical measurement techniques have been proven to be capable of label-free molecular recognition and detection, which opens a new strategy for the design of efficient single-molecule detection sensors. In this review, we outline the main advances and potentials of single-molecule electronics for qualitative identification and recognition assays at the single-molecule level. Strategies for single-molecule electro-sensing and its main applications are reviewed, mainly in the detection of ions, small molecules, oligomers, genetic materials, and proteins. This review summarizes the remaining challenges in the current development of single-molecule electrical sensing and presents some potential perspectives for this field.

Identification of procymidone in rapeseed oils based on olfactory visualization technology

A new method for qualitative identification of pesticide residues in edible oil based on olfactory visualization technology is proposed. Firstly, according to the pre-experimental experience, 12 suitable chemical dyes were selected to prepare sensor arrays to form an olfactory visualization system. The system was used to gather volatile odor information in rapeseed oil samples that had different levels of procymidone residues. Subsequently, the olfactory information was translated into a visual representation. Then, the visual data, i.e. color features, were preprocessed and underwent principal component analysis to extract and reduce the dimensionality of sensor image information, resulting in the optimal number of principal components (PCs). Finally, three different models based on the sensor image features were constructed for analysis. These models included the support vector machine (SVM), the random forest (RF) and the kernel based extreme learning machine (KELM). In process of model correction, the parameters of the three models were optimized by dung beetle optimizer (DBO) algorithm, resulting in the identification of the optimal parameters. The results show that the cumulative variance contribution increases slowly when the number of PCs starts from 6, and the three models (i.e. DBO-SVM, DBO-RF, and DBO-KELM) have the best prediction results when PCs = 6. Compared to the DBO-RF and DBO-KELM models, the training and prediction performance of the SVM model based on the DBO optimization algorithm is more stable and has the higher recognition accuracy of the samples, reaching 100%. The results show that it is feasible to use olfactory visualization technology to achieve high-precision qualitative identification of edible oil pesticide residues.

Genetic Identification and Traceability of Insect Meals.

Insects have been proposed as a rich alternative source of protein for the partial or total replacement of fishmeal in aquaculture. For maximum safety and effectiveness of insect meals, control of the quality composition of these products is considered mandatory. The aim of this study was the genetic analysis of the composition of commercially available insect meals at the species level. Commercially available Hermetia illucens, Tenebrio molitor and Musca domestica individuals, as well as nine insect meals produced from these species, were analyzed. The genetic identification of insects at the species level was based on a COI fragment, and analysis of the insect meals' composition was performed with the processes of cloning and colony PCR. Genetic analysis indicated that the commercially available larvae morphologically identified as Musca domestica belonged to the species Muscina stabulans. In the commercially available insect meals, no other animal species was identified beyond the expected one. However, in the insect meal produced for research purposes, fungal growth was detected. The used methodology, herein, allows for the qualitative genetic identification of insect meals and could be included in the methods of traceability of products containing insects and other animal species.

An Overview of the Microbiota of the Human Urinary Tract in Health and Disease: Current Issues and Perspectives.

This article is intended to deepen our knowledge to date regarding the functions of the resident microbiota/microbiome in the urinary system for human health and disease. First, we sought to report the general characteristics (composition and stability) of the normal urinary system microbiota in the different anatomical sites in relation to some factors such as the effect of age, gender and diet, analyzing in detail the functions and the composition of the microbiota in the light of current knowledge. Several pieces of evidence suggest the importance of preserving the micro-ecosystem of the urinary system, and in some cases their relationship with diseases is important for maintaining human health is well understood. The female and male reproductive microbiota have mainly been studied over the past decade. In the past, the arrest was thought to have taken place in a sterile environment. Microorganisms of the microbiota form biofilms, three-dimensional structures, that differ in the reproductive organs and interact with both gametes and the embryo as well as with maternal tissues. These biofilms from the reproductive system also interact with others, such as that of the gastrointestinal tract. Reduction in its diversity intestinal microbiota can disrupt estrogen metabolism and affect the reproductive microbiota. It is therefore understood that its quantitative and qualitative identification is important for microbiota, but also the study of the structures formed by the microorganisms. A dysbiosis with local or systemic causes can lead to serious diseases. The role of probiotics in maintaining microbial population harmony (eubiosis) and preventing certain pathologies of the urinary and reproductive system was also investigated. A negative variation in the qualitative and quantitative composition of certain strains of microorganisms (dysbiosis) due to local or systemic causes can even lead to serious diseases. The role of probiotics in maintaining the healthy balance of microorganism populations (eubiosis), and thus in the prevention of certain pathologies of the urinary and reproductive system, has also been studied.

Phytochemical studies of Xanthium Strumarium L leaves from Tisgaon (PIN Code 431002) of Taluka and District Aurangabad, India

Xanthium Strumarium L. (Family: Compositae) is a cocklebur or burweed oftentimes located as a weed in roadsides, rice fields, and hedges all through the tropical components of India. The phrase “xanthium” is derived from an ancient Greek word “Xanthos” which means yellow and “Strumarium” capability “cushion like swelling,” regarding the seedpods which flip from inexperienced to yellow as they ripen (later they come to be deep yellow to brown). It is regularly known as Chota gokhru due to the shape of its fruit which looks like the cow's toe. In many components of India, it is acknowledged as adhasisi, as this weed is used for the remedy of the common disease hemicrania. The genus Xanthium consists of 25 species, all American origin. Xanthium Spinosum Linn. and X. Strumarium Linn. are used medicinally in Europe, North America, and Brazil, Xanthium Canadiens Mill. is used in North America and Brazil and X. Strumarium Linn. in China, India, and Malaysia. Xanthium Strumarium L. showed antioxidant, anti-inflammatory, anti-cancer activities as well as various medicinal significances hence it created sufficient interest to investigate phytochemical studies of leaves of Xanthium Strumarium L. of Tisgaon (PIN Code 431002) of Taluka and District Aurangabad, India which is hither to unknown from this region. Recently phytochemical studies of leaves of Xanthium Strumarium L of Tisgaon were successfully carried out and the present work deals with the qualitative analysis and identification of elements, trace elements and organic moieties present in Xanthium Strumarium L. During this study it is investigated that the leaves of Xanthium Strumarium L contain sodium, potassium, calcium, chloride, nitrate, phosphate, fluoride, bicarbonate along with terpenoids, coumarins, steroids, glycosides, flavonoids, thiazides, anthraquinones, naphthoquinones, polyphenols and alkaloids.

A Validated, Fast and Simple, Simultaneous Determination of Captopril and Telmisartan in Laboratory Prepared Mixture for Use in Haemodialysis Patients Suffering from Inflammation

Captopril and telmisartan are widely used anti-hypertensive drugs, and their fixed dose combination is under phase IV trials. In the present study, spectrophotometry and RP-HPLC methods were successfully developed and validated per standard regulations. In UV spectrophotometry method concentrations of captopril and telmisartan in a synthetic mixture prepared in the laboratory were determined using the simultaneous equation method. The linearity was found 8 to 40 μg/mL for captopril and 5 to 25 μg/mL for telmisartan. The R2 (Coefficient of Correlation) value was found to be 0.999 for both drugs. The %assays of the conc. of captopril and telmisartan in synthetic mixture were found in an acceptable range. In the RP-HPLC method %assay was found to be 100.05 and 100.16% with %RSD value of 0.34 and 0.58 for captopril and telmisartan, respectively. The proposed UV and RP-HPLC method simultaneous estimation of captopril and telmisartan has potential application for qualitative identification as well as quantitative determination.

Qualitative Comic Book Mapping: Developing Comic Books Informed by Lived Experiences of Refugee Youth to Advance Sexual and Gender-Based Violence Prevention and Stigma Reduction in a Humanitarian Setting in Uganda

Sexual and gender-based violence (SGBV) is a persistent concern in humanitarian contexts, yet there is a dearth of SGBV prevention and post-rape clinical care interventions tailored for refugee youth. Graphic medicine, the use of images and text such as in comic books, has been employed to depict lived experiences to promote health, wellbeing, and education. Comic books provide a low-cost, youth-friendly approach to health promotion that is accessible to varying literacy levels. Limited research, however, has described the process of developing graphic medicine approaches for SGBV prevention and sexual violence stigma reduction with and for refugee youth in humanitarian settings. To address this knowledge gap, this paper shares a Qualitative Comic Book Mapping approach, whereby qualitative data alongside theoretical and empirical SGBV literature informed the development of comic book scenarios with refugee youth aged 16-24 in Bidi Bidi refugee settlement, Uganda. Steps included conducting focus groups and in-depth individual interviews with 78 community members (youth, elders, service providers) in Bidi Bidi to explore SGBV lived experiences among refugee youth in Bidi Bidi and ideas for solutions to reduce SGBV and related stigma, in addition to improving post-rape care experiences and engagement. The Qualitative Comic Book Mapping approach involved: a) thematic analysis of qualitative data and identification of overarching themes; b) aligning qualitative themes with theories of change for SGBV prevention and stigma reduction; and c) co-developing comic book scenarios with refugee youth peer navigators and community experts to integrate SGBV prevention and stigma reduction theory with refugee youth lived experiences. The final comic book involved five youth-focused scenarios and was integrated in an intervention with refugee youth, including providing youth with a blank version of the comic book to complete themselves. We share how theoretically-informed comic books can be developed from qualitative data with refugee youth in a humanitarian setting.

Karakterisasi Bobot Jenis dan Identifikasi Kalsium Pada Susu Kedelai

Soy milk is a drink that is good for health because it contains protein, unsaturated fatty acids, vitamins, carbohydrates and important minerals needed by the body such as calcium. Calcium functions to help the process of forming bones and teeth, blood clotting, muscle contraction, transmitting nerve cell signals, maintaining bone density, supplements in lowering LDL cholesterol levels. This study aims to determine the density characteristics of soy milk produced and to identify the calcium content qualitatively in soy milk drinks circulating in Pontianak City. The research method used in this study is descriptive non-experimental using 3 samples of soy milk circulating in Pontianak City with purposive sampling technique. Sample preparation in this study was carried out by wet digestion using HNO3 65% p.a solvent. Qualitative identification of calcium content was carried out using 5% ammonium oxalate reagent. The presence of a white precipitate after the addition of 5% ammonium oxalate indicates a positive calcium in the sample. Characterization of the specific gravity of soy milk was carried out using a pycnometer. The results obtained in this study are samples of soy milk A, B and C are positive for calcium because they form a white precipitate when soy milk samples are added with 5% ammonium oxalate reagent and the density of soy milk A, B and C is 1, respectively. ,00894905 g/mL, 1.008108836 g/mL and 1.008789684 g/mL.

A novel strategy for identification of pesticides in different categories by concentration-independent model based on a nanozyme with multienzyme-like activities

Conventional rapid detection methods are difficult to identify or distinguish various pesticide residues at the same time. And sensor arrays are also limited by the complexity of preparing multiple receptors and high cost. To address this challenge, a single material with multiple properties is considered. Herein, we first found that different categories of pesticides have diverse regulatory behaviors on the multiple catalytic activities of Asp-Cu nanozyme. Thus, a three-channel sensor array based on the laccase-like, peroxidase-like, and superoxide dismutase-like activities of Asp-Cu nanozyme was constructed and successfully used for the discrimination of eight kinds of pesticides (glyphosate, phosmet, isocarbophos, carbaryl, pentachloronitrobenzene, metsulfuron-methyl, etoxazole, and 2-methyl-4-chlorophenoxyacetic acid). In addition, a concentration-independent model for qualitative identification of pesticides has been established, and 100% correctness was achieved in the recognition of unknown samples. Then, the sensor array also exhibited excellent interference immunity and was reliable for real sample analysis. It provided a reference for pesticide efficient detection and food quality supervision.

Detection of antioxidants in edible oil by two-dimensional correlation spectroscopy combined with convolutional neural network

In this study, the qualitative identification and quantitative prediction of antioxidants in edible oil were achieved by the two-dimensional correlation spectroscopy combined with convolutional neural network (CNN). The CNN model obtained classification accuracy of 97.56%, which is significantly higher than that of the partial least squares discriminant analysis (PLS-DA) model. Comparing the performance of the established partial least squares (PLS), CNN, and CNN combined with PLS (CNN_PLS) regression models, it is found that the CNN model has the best quantitative performance among the three regression models, with a root mean square errors for prediction (RMSEP) of 0.0232 and a coefficient of determination for prediction (R2p) of 0.9703. At the same time, it is found that the prediction ability of the CNN_PLS model is affected by the feature extraction ability of CNN models and the output dimension of convolution layers in CNN models. The results demonstrate the potential of the proposed method for the detection of antioxidant in edible oils.

Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection.

The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935-1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.