Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification

Pranoto, Wawan Joko; Al‐Shawi, Sarmad Ghazi; Chetthamrongchai, Paitoon; Chen, Tzu-Chia; Petukhova, Ekaterina; Nikolaeva, Natalia; Abdelbasset, Walid Kamal; Yushchenko, Natalya; Aravindhan, Surendar

doi:10.1590/fst.80921

Cited by 11 publications

(8 citation statements)

References 47 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PLS method was used to establish models for the spectral data of ROI with different shapes and sizes (Pranoto et al, 2022;Rambo et al, 2020), and the results are shown in Table 3. The correlation coefficient and RMSE of the calibration set and the prediction set were improved with the expansion of the selected area for the circular and square ROIs.…”

Section: Prediction Results Based On the Pls Modelmentioning

confidence: 99%

Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology

Luo

ZHANG

et al. 2022

Food Sci. Technol

View full text Add to dashboard Cite

This paper explores the influence of the selection method of the region of interest (ROI) on the results in the total sugar of apple detection based on hyperspectral imaging technology. Taking Fuji apple as the detection object, the hyperspectral images of the samples were collected based on the 900~1750 nm hyperspectral imaging system, and the total sugar content of the samples was obtained based on the anthrone colorimetric method. The square ROI and circular ROI of different sizes were extracted. The average spectrum of the region was used to establish a quantitative analysis model of apple's total sugar content by partial least squares (PLS). The results show that apple's total sugar detection model established by extracting a circular ROI with a diameter of 25 pixels has the highest accuracy and strongest prediction ability(R c = 0.8977, RMSEC = 0.6459, R P = 0.8836, RMSEP = 0.6627). The research shows that selecting ROI with a suitable shape and size for the research object is of great significance for improving the accuracy of the prediction model of apple's total sugar content and giving play to the advantages of hyperspectral images.

show abstract

Section: Prediction Results Based On the Pls Modelmentioning

confidence: 99%

Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology

Luo

ZHANG

et al. 2022

Food Sci. Technol

View full text Add to dashboard Cite

show abstract

“…NIR spectroscopy has been used in the tea industry, for example, to analyze caffeine and free amino acids, and determine the origins of tea varieties. Backward interval partial least squares (bi-PLS) (Wang et al, 2022b) and back propagation-artificial neural network (BP-ANN) algorithms (Xu et al, 2022;Pranoto et al, 2022), and other pattern recognition systems (Shi et al, 2012), such as the support vector machine, have been used to calibrate tea nutrition models. However, there are few reports on near infrared spectroscopy combined with chemometrics to identify fresh tea leaves at different altitudes, so it is necessary to carry out relevant research.…”

Section: Fast and Nondestructive Discrimination Of Fresh Tea Leaves A...mentioning

confidence: 99%

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

et al. 2023

View full text Add to dashboard Cite

Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.

show abstract

“…Because of advances in spectroscopy and the development of novel sensor technologies such as spectrophotometers and cameras, non-destructive and rapid assessments of quality-related factors in foods are becoming more common (Pranoto et al, 2022). In the past few decades, a good number of rapid nondestructive detection technologies have been developed for the safety and quality detection of various agricultural products, one of which is the Synchronous fluorescence spectrum (Xin et al, 2022;Mahmudiono et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Fluorescence spectroscopy is also reported to be used for monitoring the structural changes during cheese manufacturing (Muhammad et al, 2019). Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification (Pranoto et al, 2022). Rapid identification of sunflower seed oil quality by three-dimensional synchronous fluorescence spectrometry (Shuang-Fang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A rapid identification method of rice protein in rice starch based on synchronous fluorescence spectroscopy

ZHANG

Liu

et al. 2023

Food Sci. Technol

View full text Add to dashboard Cite

Rice protein is a protein with high nutritional value and its essential amino acids are complete. Rice protein content is related to the Nutritional value of rice powder mixture. Three-dimensional synchronous fluorescence spectroscopy combined with Parallel factor analysis (PARAFAC) and Partial least squares (PLS) was applied for Protein content in rice starch. Synchronous Fluorescence Spectroscopy were obtained at 250-400 nm (excitation) and 260-750 nm (wave-length deviation). Firstly, data of Rice starch were collected using synchronous fluorescence spectroscopy equipment and the protein content of samples were got by Dumas Nitrogen analyzer ; secondly, three improved PLS methods, including interval partial least squares (iPLS), backward interval partial least squares (biPLS) and synergy interval partial least squares (siPLS),were used to find the most informative range. Lastly, models with better predictive turbidity of fragrance vinegar using PLS was established in the whole wavelength range .The results showed that the models by siPLS method, which separated the whole spectra into 10 intervals, and combine four intervals of [1 3 4 5]had the best predictive ability, the RMSECV and RMSEP were 0.1482 and 0.1696, and the calibration and prediction correlation coefficient were 0.8513 and 0.8236. Using three-dimensional synchronous fluorescence spectroscopy for rapid detection of Protein content in rice starch was feasible.

show abstract

Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification

Cited by 11 publications

References 47 publications

Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology

Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

A rapid identification method of rice protein in rice starch based on synchronous fluorescence spectroscopy

Contact Info

Product

Resources

About