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Enlargement of Gold Nanoparticles for Sensitive Immunochromatographic Diagnostics of Potato Brown Rot
摘要： Lateral ?ow immunoassay (LFIA) is a convenient tool for rapid ?eld-based control of various bacterial targets. However, for many applications, the detection limits obtained by LFIA are not suf?cient. In this paper, we propose enlarging gold nanoparticles’ (GNPs) size to develop a sensitive lateral ?ow immunoassay to detect Ralstonia solanacearum. This bacterium is a quarantine organism that causes potato brown rot. We fabricated lateral ?ow test strips using gold nanoparticles (17.4 ± 1.0 nm) as a label and their conjugates with antibodies speci?c to R. solanacearum. We proposed a signal enhancement in the test strips’ test zone due to the tetrachloroauric (III) anion reduction on the GNP surface, and the increase in size of the gold nanoparticles on the test strips was approximately up to 100 nm, as con?rmed by scanning electron microscopy. Overall, the gold enhancement approach decreased the detection limit of R. solanacearum by 33 times, to as low as 3 × 104 cells·mL–1 in the potato tuber extract. The achieved detection limit allows the diagnosis of latent infection in potato tubers. The developed approach based on gold enhancement does not complicate analyses and requires only 3 min. The developed assay together with the sample preparation and gold enlargement requires 15 min. Thus, the developed approach is promising for the development of lateral ?ow test strips and their subsequent introduction into diagnostic practice.
关键词： gold nanoparticles,immunochromatographic diagnostics,potato brown rot,gold particle growth,increase of sensitivity,lateral ?ow immunoassay,test strips,Ralstonia solanacearum
更新于2025-09-23 15:22:29
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Ina??season potato yield prediction with active optical sensors
摘要： Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha?1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha?1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
关键词： sensor technology,petiole sampling,potato,prediction models,multiple regression analysis,Yield prediction,nitrogen loss
更新于2025-09-23 15:21:01
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[Advances in Food and Nutrition Research] || Advanced Analysis of Roots and Tubers by Hyperspectral Techniques
摘要： Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
关键词： Gradation,Physical properties,Chemometric analyses,Multivariate statistics,Hyperspectral imaging,Chemical constituents,Authentication,Vibrational spectroscopy,Potato tuber,Food quality
更新于2025-09-19 17:15:36
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Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy
摘要： Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy were used to identify potato varieties and detect potato doneness degree. The varieties of potato tubers can be successfully classified by hierarchical cluster analysis (HCA). The partial least squares regression (PLSR) model exhibited good prediction result for the doneness degree evaluation. Principal component and first-derivative iteration algorithm (PCFIA) was introduced to select feature variables instead of using the full wavelength spectra for modelling. Based on two sets of feature variables selected from NIR and MIR regions, both NIR–PCFIA–HCA and MIR–PCFIA–HCA showed higher performances of hierarchical clustering. Moreover, NIR–PCFIA–PLSR and MIR–PCFIA–PLSR models were effectively used to predict tuber doneness degree, yielding the RP as high as 0.935 and the RMSEP as low as of 0.503. It is concluded that the PCFIA is an effective approach for feature variable selection, and both NIR and MIR spectroscopic techniques are capable of classifying potato varieties and determining potato doneness degree.
关键词： HCA,ATR-MIR,Potato,Variable selection,PLSR,NIR
更新于2025-09-19 17:15:36
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Laser-light backscattering imaging approach in monitoring and classifying the quality changes of sweet potatoes under different storage conditions
摘要： Optical imaging techniques have gained wide attention for quality detection of agricultural and food products. In this work, the non-destructive ability of the laser-light backscattering imaging technique (LLBI) for monitoring and classifying the quality changes of sweet potatoes under different storage conditions was investigated. Freshly-harvested sweet potato root samples were stored at 5 °C, 15 °C and 30 °C for a period of 21 d with 120 samples in each storage group. Laser diode emitting light at 658 nm wavelength along with the camera system was employed to capture the backscattered light from the subjected samples. The acquired backscattering images were then pre-processed and segmented, and the intended backscattering parameters (BP) were extracted. Quality parameters (QP) such as moisture content (MC), soluble solids content (SSC), texture and color properties (L*, a*, b*) were measured using the conventional methods as standard reference data. Multivariate analysis in terms of partial least squares regression (PLSR), principal component analysis (PCA) and linear discriminant analysis (LDA) was carried out to correlate and classify the sweet potatoes based on the BP. Results showed that storage had a significant effect both in the BP and QP of sweet potatoes as well as in the interaction between the BP and the treatments (day and temperature) applied. Among all the QP, SSC gave the most promising results (R = 0.56-0.66; RMSE = 0.76–1.10) across all the storage conditions. The analysis also revealed that 15 °C was the most suitable storage condition with the favourable PLSR results (R > 0.50) in all the examined parameters. Moreover, variations on the BP of the samples with respect to the different storage conditions were correctly classified with over 90 % and 80 % accuracies using the PCA and LDA, respectively. Thus, the study indicates that the LLBI technique is feasible and can be a useful tool for a non-destructive quality measurement and classification of sweet potatoes under different storage conditions.
关键词： Storage,Sweet potato,Quality,Backscattering imaging,Temperature
更新于2025-09-19 17:13:59
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Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis
摘要： Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2 p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2 p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.
关键词： water content,browning,hyperspectral imaging,fresh-cut potato tuber slices,color index
更新于2025-09-19 17:13:59
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[IEEE 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS) - Saint Petersburg, Russia (2018.9.24-2018.9.28)] 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS) - Algorithms for Detecting Potato Defects Using Images in the Infrared Range of Spectrum
摘要： An automated system for contactless thermal quality testing of potato moving along a chain conveyor is presented. The algorithm of the computer vision system for the recognition of potato defects in real time is described. The method for detecting defects is based on determining the temperature difference between healthy and damaged tissues after short-term heating of the tubers.
关键词： noncontact,computer vision,nondestructive,potato,testing,defect,infrared
更新于2025-09-10 09:29:36