Variable selection for the determination of the soluble solid content of potatoes with surface impurities in the visible/near-infrared range

Han, Minjie; Wang, Xiangyou; Xu, Yingchao; Cui, Yingjun; Wang, Liang; LV, Danyang; Cui, Lixia

doi:10.1016/j.biosystemseng.2021.06.019

Cited by 28 publications

(5 citation statements)

References 25 publications

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“…After feature wavelength screening, the principal component analysis (PCA) dimensionality reduction approach was utilized in this work to further reduce the dimensionality of the data. Using mapping, PCA rearranged the data while keeping the primary spectral characteristics and condensing the many feature variables into a handful of key features [ 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Zhang,

He,

Yang

et al. 2023

Sensors

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Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

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Section: Methodsmentioning

confidence: 99%

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Zhang,

He,

Yang

et al. 2023

Sensors

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“…Before NIR-based quality assessments, potatoes (and other agricultural products) should be cleaned to reduce signals caused by surface impurities (e.g., soil) [ 45 ]. Impurities on the potato surface (such as soil) have lower absorption and higher reflection, while the inner parts of the potato have higher absorption due to their high moisture and dry matter [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

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Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

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“…In Vis/NIR spectroscopy, after plotting a spectrum from 350 to 1100 nm, the wavelength ranges below 400 nm and above 1000 nm are often noisy or show interference, so it is expected to cut them manually. Decreasing wavelength points can speed up spectral detection but also reduce resolution and slightly affect the model's accuracy [12].…”

Section: Vis/nir Spectral Data Of Potato Tubermentioning

confidence: 99%

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

Prasetyo,

Amanah,

Farras

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Conventional methods of detecting Fusarium spp. infection, which causes significant economic losses in potato production, are time-consuming and expensive. This study explored rapid and non-destructive detection techniques using visible/near-infrared (Vis/NIR) spectroscopy. Potato seeds of the Granola L variety were intentionally infected with Fusarium spp. by fungal inoculation, then stored at 12°C, 25°C, and a combination of both. Healthy potatoes were stored under the same conditions in containers for 30 days and monitored every five days. Principal component analysis-linear discriminate analysis (PCA-LDA) was used to classify potato tubers based on their infection status. PCA-LDA analysis revealed significant spectral differences between healthy and infected potato seeds across all storage temperatures. Calibration reliability values were 95.87% (for samples stored at 12°C), 97.52% (stored at 25°C), and 98.35% (for the combination of 12°C and 25°C). Similar trends were observed for accuracy: 91.96% (12°C), 98.29% (25°C), and the highest accuracy of 98.65% for the combined temperature. These techniques facilitate rapid identification of infections, aiding farmers and producers in implementing more efficient preventive actions, resulting in decreased crop losses and waste products and enhanced productivity in the agricultural sector.

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Variable selection for the determination of the soluble solid content of potatoes with surface impurities in the visible/near-infrared range

Cited by 28 publications

References 25 publications

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

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