Near-Infrared Hyperspectral Imaging Rapidly Detects the Decay of Postharvest Strawberry Based on Water-Soluble Sugar Analysis

Qiang, Liu; Wei, Kangli; Xiao, Hui; Tu, Sicong; Sun, Ke; Sun, Yi; Pan, Leiqing; Tu, Kang

doi:10.1007/s12161-018-01430-2

Cited by 42 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strawberry, which is widely known as the queen of fruit, is popular worldwide for its good taste and rich nutrients [1]. Strawberry is not only rich in sugar, acid, and cellulose but also contains vitamins, such as vitamin C (VC), which helps promote the synthesis of fats and proteins and maintain the immune function of humans [2].…”

Section: Introductionmentioning

confidence: 99%

“…Hyperspectral imaging (HSI) combines the advantages of machine vision and spectroscopy to provide spectral and spatial information about the external and internal qualities of food, which has been applied in defects of peaches [13], the color of sausage [14], the SSC and acidity of limes [15], and the VC of jujube [7]. In addition, many researchers have used HSI to study the qualities of strawberries, including its moisture content, pH, SSC, and firmness [4,16,17], bruise and fungi contamination [18,19], ripeness [3,20], foliar anthracnose [21], and decay [1]. In these studies, however, only the spectra extracted from the HSI images were used for the above analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Weng

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Weng

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Besides, with the increase in wavelength, the correlation between soluble sugars and μ a increased, while the correlation between soluble sugars and μ ′ s decreased. It could be inferred that μ a was more associated with chemical components, while μ ′ s was less correlated with them [ 7 , 30 , 36 , 46 ]. Furthermore, previous study presented that scattering property was influenced by microstructure.…”

Section: Resultsmentioning

confidence: 99%

“…Rowe et al [ 26 ] measured the optical spectra of apples over the range of 400–1050 nm using IS and obtained an average r of–0.68 between the μ ′ s and firmness. Liu et al showed that μ ′ s of kiwifruit had stronger correlation with firmness ( r = 0.74–0.87) than with SSC ( r = 0.49–0.81) and moisture content ( r = 0.59–0.80) [ 7 ]. These studies indicated that the absorption and scattering properties had potential to determine the internal quality of apple.…”

Section: Resultsmentioning

confidence: 99%

“…Light transfer in tissues contains absorption or scattering, and the process of interaction mainly depends on the different chemical components and structures of tissues. By collecting light that is reflected back from or transmitted through the tissue based on NIR and HSI techniques, the chemical as well as structural characteristics of tissues can be acquired [ 7 ]. The prediction models for SSC can be built based on chemometrics methods by using the captured spectra and the standard measurements, and some satisfactory results have been obtained [ 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optical Absorption and Scattering Properties at 900–1650 nm and Their Relationships with Soluble Solid Content and Soluble Sugars in Apple Flesh during Storage

Wei

Feng

et al. 2020

Foods

Self Cite

View full text Add to dashboard Cite

Soluble solid content (SSC) is regarded as the most significant internal quality associated with the taste and maturity in fruits. Evaluating the relationship between the optical properties and soluble sugars facilitates exploration of the mechanism of optical techniques in SSC assessment. In this research, absorption coefficient (μa) and reduced scattering coefficient (μ′s) of Fuji apple during storage were obtained using automatic integrating sphere (AIS) at 905–1650 nm. Relationships between μa, μ′s and SSC, and soluble sugars contents, were investigated. The result showed that SSC, the content of total soluble sugars (TSS), fructose, glucose and sucrose were all decreasing after storage, and the same trend appeared in the change of μa and μ′s. In the whole wavelength range, both μa and μ′s were positively related to SSC and soluble sugars contents. The correlations between μa and SSC, and soluble sugars contents, showed increasing tendencies with increasing wavelengths, while for μ′s, correlations had the opposite trend. The strongest correlations between μa and SSC, and soluble sugars contents, were observed in the correlation of μa with sucrose, with an r of 0.934. Furthermore, a partial least square (PLS) model for sucrose based on μa was built with the coefficient of determination of prediction (Rp2) and the root mean square error of prediction (RMSEP) of 0.851 and 1.047, respectively. The overall results demonstrate that optical properties at the range of 905–1650 nm could be used to evaluate SSC of apples and this may due to the strong correlation between sucrose content and μa.

show abstract

Automated Sorting, Grading of Fruits Based on Internal and External Quality Assessment Using HSI, Deep CNN

Ganesh¹,

Priyatharshini²,

Kumar³

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Near-Infrared Hyperspectral Imaging Rapidly Detects the Decay of Postharvest Strawberry Based on Water-Soluble Sugar Analysis

Cited by 42 publications

References 30 publications

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Optical Absorption and Scattering Properties at 900–1650 nm and Their Relationships with Soluble Solid Content and Soluble Sugars in Apple Flesh during Storage

Automated Sorting, Grading of Fruits Based on Internal and External Quality Assessment Using HSI, Deep CNN

Contact Info

Product

Resources

About