Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches

Sun, Yi; Gu, Xuefan; Sun, Ke; Hu, Haijiang; Xu, Miao; Wang, Zhengjie; Tu, Kang; Pan, Leiqing

doi:10.1016/j.lwt.2016.10.006

Cited by 69 publications

(30 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various spectral preprocessing methods were investigated for building partial least squares (PLS) models and SVM models, including Savitzky-Golay (SG) Smoothing, Median Filter Smoothing, Moving Average Smoothing, Gaussian Filter Smoothing, Normalization, Multiple Scattering Correction (MSC) and Standard Normal Variate (SNV) methods. Pretreatment techniques, such as SG smoothing, MSC, SNV and normalization, were used to remove noise and other factors included in the spectra [20,21] . The spectra processed by SG smoothing and MSC methods had similar curve shapes as the raw spectrum, whereas spectra processed by the SNV and normalization methods had different curve shapes from the raw spectrum.…”

Section: Spectral Data Analysismentioning

confidence: 99%

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

Li¹,

Hu²

2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

Potato late blight, which is caused by Phytophthorainfestans (Mont.) de Bary, is a worldwide devastating disease for potato. It decreased yields of potato and caused unpredictable losses all over the world. Various simple statistical methods and forecasting models have been developed to predict and manage potato late blight. Meanwhile, there is a rising need to develop prediction models reflecting peroxidase (POD) activity, which is an important health index that varies with infection and correlated with stress resistance in plants. Thus, the aim of this research was to develop kinetic models to predict POD activity. Infection-induced changes in potato leaves stored in an artificial climate chest at 25°C were analyzed using hyperspectroscopy. Four prediction models were developed by using linear partial least squares (PLS) and nonlinear support vector machine (SVM) methods based on the full spectrum and effective wavelengths. The effective wavelengths were selected by the successive projection algorithm (SPA). In this study, the prediction model developed by means of SPA-SVM method obtained the best performance, with a R p (correlation coefficient of prediction) value of 0.923 and a RMSEp (root mean square error of prediction) value of 24.326. Five-order kinetics models according to the prediction model were developed, and late blight disease can be predicted using this model. This study provided a theoretical basis for the prediction of latencies of late blight.

show abstract

Section: Spectral Data Analysismentioning

confidence: 99%

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

Li¹,

Hu²

2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…[21,22] A typical hyperspectral image consists of a series of images of different wavelengths, and each pixel of image is a spectrum on this position, which covers the Vis/NIR range. HSI has been used to detect many important quality attributes of agricultural materials, such as color of sausage, [23] defect on jujube, [24] allicin, and soluble solid content of garlic, [25] chilling injury of peaches, [26,27] SSC and firmness of pear, [28] contaminants on wheat [29] and internal qualities of apples. [30] Therefore, the use of hyperspectral imaging has great potential for quality assessments of agricultural materials.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

Weng

Dong

et al. 2020

International Journal of Food Properties

View full text Add to dashboard Cite

In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (R p 2 ) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with R p 2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries. ARTICLE HISTORY

show abstract

“…Hyperspectral imaging, combining conventional two-dimensional imaging and spectroscopic techniques, produces a three-dimensional image data cube, and records the spatial and spectral information simultaneously [13]. In recent years, many researchers have demonstrated the feasibility of hyperspectral imaging systems in detecting skin defects of fruit [14,15]: bruise on apple and pear [4,16], chilling injury in peaches [17,18], decay in citrus [5,19], fungal infections in strawberry [20], and green mold pathogens on lemons [21] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Pham

Liou

2020

Applied Sciences

View full text Add to dashboard Cite

A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

show abstract

Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches

Cited by 69 publications

References 23 publications

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Contact Info

Product

Resources

About