Study of Fluorescence Spectrum for Measurement of Soluble Solids Content in Navel Orange

Li, Jing; Xue, Long; Liu, Mu Hua; Wang, Xiao; Luo, Chun

doi:10.4028/www.scientific.net/amr.186.126

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies had shown that UV light at certain wavelengths could produce fluorescence emissions which contain information about the physicochemical changes of the fruit. Li et al (2011) reported that the excitation of UV light onto navel orange fruit at a wavelength of 365 nm was able to produce emissions identified at wavelengths 385-414 nm. Fluorescence spectroscopy could be developed for non-destructive measurement of the fruit maturity index of satsuma mandarin (Citrus unshiu Marc) (Muharfiza et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Estimation of Maturity Level of ‘Crystal’ Guava Fruit by Means of Fluorescence Spectroscopy

PERTIW,

HUTOMO,

WIDODO

2023

INMATEH

View full text Add to dashboard Cite

This research aims to explore the potential use of fluorescence spectroscopy combined with chemometrics for predicting the maturity level of crystal guava fruits non-destructively. The physicochemical properties including total soluble solids (TSS), water content, firmness, and pH were obtained from laboratory tests and used as a reference in developing the predictive model. The fluorescence emission spectra under excitation of 365 nm UV LED were used as predictors. The fluorescence spectra were acquired and recorded using a miniaturized UV-Vis spectrophotometer with an effective 340 – 800 nm range. In total, 240 sets of data from crystal guava fruit samples with varying ages of 90-106 DAA (days after anthesis) were used for calibration and validation. A multivariate analysis using Partial Least Squared Regression (PLSR) was used to develop the predictive models. Several data preprocessing methods were applied to enhance the information contained in the spectral data to find the best predictive model. Analysis results showed that the developed model could accurately predict water content with rcal=0.94; SEC=0.08%, rval=0.84; SEP=0.08%; RPD=2.59, followed by TSS with rcal =0.91; SEC = 0.47%Brix, rval =0.79; SEP=0.48%Brix; RPD=2.13. Although with lower accuracy, it also could predict firmness with rcal =0.86; SEC=0.43 kgf, rval =0.74; SEP=0.43 kgf; RPD=1.82 and pH with rcal =0.81; SEC=0.10, rval =0.79; SEP=0.09; RPD=1.76. The results indicate that fluorescence spectroscopy could be used as an alternative method for non-destructive estimation of physicochemical properties as indicators of the maturity level of crystal guava fruits.

show abstract

Section: Introductionmentioning

confidence: 99%

Non-Destructive Estimation of Maturity Level of ‘Crystal’ Guava Fruit by Means of Fluorescence Spectroscopy

PERTIW,

HUTOMO,

WIDODO

2023

INMATEH

View full text Add to dashboard Cite

show abstract

“…In recent years, with the rapid development of sensors, non-destructive detection technology has been used in fruit quality evaluation. For example, multispectral [ 4 , 5 ], fluorophore [ 6 , 7 , 8 ], near-infrared spectroscopy [ 9 , 10 , 11 , 12 ], electronic nose [ 13 , 14 , 15 ], and dielectric technology [ 16 , 17 ] have been applied for the evaluation of the soluble solid content in fresh fruits. Among these technologies, near-infrared spectroscopy is currently the most widely used method for evaluating fresh fruit SSC due to its fast, simple, and non-destructive characteristics.…”

Section: Introductionmentioning

confidence: 99%

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Yang

Gao

Yan

et al. 2020

Sensors

View full text Add to dashboard Cite

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

show abstract

Study of Fluorescence Spectrum for Measurement of Soluble Solids Content in Navel Orange

Cited by 2 publications

References 7 publications

Non-Destructive Estimation of Maturity Level of ‘Crystal’ Guava Fruit by Means of Fluorescence Spectroscopy

Non-Destructive Estimation of Maturity Level of ‘Crystal’ Guava Fruit by Means of Fluorescence Spectroscopy

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Contact Info

Product

Resources

About