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a Distribution of normalized DMC in different sections of different potatoes, microscopic images of b section No. 9 and c section No. 14 in potato number 1
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Potatoes are generally consumed directly as a staple food or used for processing, depending on the quality of raw materials. Dry matter content (DMC) is the most critical characteristic of potatoes, as it determines the processing and the final product quality. This study aimed to investigate the potential of different optical sensing systems in pr...
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Introduction
Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of theentire batch of po...
Citations
... It serves as a fundamental dietary component in numerous developed and developing nations, contributing to its status as a staple food. Potatoes are ingested in their uncooked state as a fundamental sustenance or vegetable, transformed into French fries, crisps, and additional culinary enhancements, and employed in the production of potato flour, starch, and alcohol [1]. According to the data provided by the Food and Agriculture Organization (FAO), the global production of potatoes amounted to a significant quantity of 376 million metric tons. ...
... This metric effectively determines the predictive capacity of the X variables regarding the Y variables. The range of values for the coefficient of determination (R 2 ) is between zero (0) and one (1) and has a small Root Mean Square Error (RMSE) value. Implementing a calibration exhibiting an R2 value ranging from 0.83 to 0.90 in most use cases may be feasible, and a value above 0.92 is considered utilizable. ...
Conventional methods of quantifying the chemical content of potatoes at different storage temperatures are time-consuming and expensive. This research studied the Visible and Near Infrared (Vis-NIR) spectroscopy for possible rapid and nondestructive methods. In this study, healthy and Fusarium spp. Potato seeds of Granola L varieties were infected artificially through the process of inoculation of fungi, and healthy potatoes were stored in various post-harvest storage conditions, namely temperatures 12°C, 25°C and a combination of temperatures 12°C and 25°C. VIS-NIR spectral data from seeds are observed periodically during the storage period. The study results showed that Vis-NIR predicted sucrose content in potatoes. The best-developed PLSR calibration model for potatoes stored at 25°C and a combination of 12°C and 25°C show R2c of 0.87 and 0.83 and RMSEC of 0.26 and 0.28. The models also successfully predicted the sugar content of potato stored at 25°C and a combination of temperatures 12°C and 25°C with R2p 0.75 and 0.78, RMSEP of 0.36 and 0.32, and RPD of 1.99 and 2.81 for sucrose. The developed model of sucrose content or potato storage temperatures of 12°C is not recommended for monitoring and detection due to the low RPD < 1.9 even though the R2c values are 0.65 – 0.9. the results of this investigation indicate that VIS-NIR spectroscopy could potentially serve as a tool for quantifying the chemical composition of potatoes during post-harvest storage.
... This technique allowed the neural network to learn the intrinsic characteristics of the sample data and for the categories to be differentiated in-depth, which enhanced the model's robustness and minimized overfitting. However, due to practical experimental constraints, collecting a significant amount of data at once often poses a challenge, which can be detrimental to deep-learning models [22]. ...
More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R²P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R²P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.
... Along with increased spectra quality from the millions of spectral data points acquired at each wavelength, NIR-HSI also gives information on the spatial distribution of the target product's chemical components. Numerous root and tuber crops, particularly potatoes and sweet potatoes, have been reported to use NIR-HSI for their food quality assessment [26,[75][76][77][78][79][80][81][82]. ...
Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology.
Yams (Dioscorea spp.) are important food and commercial crops in West African countries. They contribute significantly to global food production and provide dietary energy. The quality of yam food products depends on specific internal and external parameters, such as the DMC and other biochemical traits. However, measuring these traits can be challenging, particularly when analyzing many genotypes. This study aimed to evaluate the feasibility of using near-infrared (NIR) hyperspectral imaging (932–1721 nm) along with machine learning to rapidly measure the dry matter content (DMC) of fresh, intact yam tubers. Hyperspectral images were acquired across the yam tuber’s cross-sections, and the resulting spectra from the images were averaged and preprocessed. Partial least square regression (PLSR) combined with successive progressions algorithms (SPA), Competitive Adaptive Reweighted Sampling (CARS), Artificial Neural network (ANN) and Boruta algorithms (BA) were used
to select the important wavelengths for developing a prediction model for DMC (g/100 g). The PLSR-SPA-CARS model showed the most accurate prediction performances with a coefficient of determinations in calibration (R2cal) and prediction (R2pred) of 0.974 and 0.958, respectively, and low root mean square error (RMSEP) of 0.898 g/100 g. The distribution of DMC was visually represented by projecting the developed model to generate
color chemical maps. This study resolves that NIR hyperspectral imaging can rapidly assess the DMC of fresh, intact yam tubers.
The application of hyperspectral imaging (HSI) has gained significant importance in the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to conduct a systematized review by analysing the most relevant compounds, diseases and stress factors in potatoes using hyperspectral imaging. For this purpose, scientific studies were retrieved through a systematic keyword search in Web of Science and Scopus databases. Studies were only included in the review if they provided at least one set of quantitative data. As a result, a total of 52 unique studies were included in the review. Eligible studies were assigned an in-house developed quality scale identifying them as high, medium or low risk. In most cases the studies were rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained better results using linear methods. In addition, a meta-analysis of studies based on regression and classification was attempted but was not possible as not enough studies were found for a specific variable.
