Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles

Vásquez, Nadya; Magán, Claudia; Oblitas, Jimy; Chuquizuta, Tony; Ávila-George, Himer; Castro, Wilson

doi:10.1016/j.jfoodeng.2017.09.008

Cited by 70 publications

(55 citation statements)

References 35 publications

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“…Similar to Castro et al () and Vásquez et al (), in this study, to avoid the effects of stray light, all main components of the hyperspectral imaging system (except computer) were combined in a big dark box. Figure showed the schematic diagram of the main components.…”

Section: Methodsmentioning

confidence: 99%

“…The basis for the calculation was as shown in Equation (1), where X referred to moisture content of the samples, M 1 referred to the total weight of aluminum specimen box and sample, M 2 referred to the total weight of dried sample and aluminum specimen box, and M 3 referred to the weight of aluminum specimen box. Similar to Castro et al (2018) and Vásquez et al (2017), in this study, to avoid the effects of stray light, all main components of the hyperspectral imaging system (except computer) were combined in a big dark box. Figure 2 showed the schematic diagram of the main components.…”

Section: Sample Preparationmentioning

confidence: 99%

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Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Sun

Shi

Zhang

et al. 2019

J Food Process Engineering

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Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push‐broom” system to acquire hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels. Practical applications The quality of peanut has a direct relationship with the moisture content in peanut. If moisture content in peanut is over standard, the peanut storage time will be shorter and also easy to be bad. It is harmful to eat this kind of peanut. Traditional methods for detection of peanut moisture content are tedious, time‐consuming, and even greatly influenced by subjective factors. As an emerging technique, hyperspectral imaging technology can detect moisture content in peanut kernels rapidly, accurately, and nondestructive. In this study, we generated a best prediction model, successive projections algorithm–support vector machine regression, which was established based on several characteristic wavelengths. All these results provided a theoretical basis for the detection of portable moisture content in peanut kernels.

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Section: Methodsmentioning

confidence: 99%

Section: Sample Preparationmentioning

confidence: 99%

Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Sun

Shi

Zhang

et al. 2019

J Food Process Engineering

View full text Add to dashboard Cite

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“…Cheese is one of the most popular and the oldest dairy product. It is a great source of valuable nutrients and minerals (Vásquez et al , ). It can be consumed separately or as a part of various foods.…”

Section: Introductionmentioning

confidence: 99%

The effect of native and polymerised whey protein isolate addition on surface and microstructural properties of processed cheeses and their meltability determined by Turbiscan

Nastaj

Terpiłowski

Sołowiej

2020

Int J of Food Sci Tech

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The aim of this paper was to investigate the effect of whey protein isolate (WPI) and polymerised whey protein isolate (WPP) addition on physicochemical properties of processed cheese with partial fat replacement. Surface properties were determined by advancing (h a ) and receding (h r ) contact angles, apparent free surface energy (c s ). Roughness measurements, cheese meltability and Turbiscan Stability Index (TSI) were evaluated by Turbiscan. Higher WPP concentrations produced the more hydrophobic surfaces of the samples. Conversely, increase in WPI addition promoted the hydrophilic cheese surfaces. The effect of roughness was much larger for samples containing WPI. The increase in protein addition caused a decrease in meltability and an increase in stability of cheeses. The optical and confocal images have proved that cheeses with WPP demonstrated more packed and denser structure in comparison to samples with WPI.Surface properties of processed cheese M. Nastaj et al.

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“…Verification of buffalo's milk authenticity is generally based on the identification of single markers or groups of markers, such as proteins and their fractions, peptides and amino acids by electrophoretic, chromatographic and immunological techniques. [13][14][15][16][17][18][19][20] Although these techniques have good sensitivity and generate accurate and reliable results, they are expensive, time consuming, need specialists trained in conducting laboratory activities, use chemical reagents harmful to handlers and the environment, making it difficult to implement them in monitoring programs of industries. 15 Thus, it is necessary to adapt techniques, especially nondestructive, through appropriate control methods, to be used in association with traditional/official methodologies to identify the type of milk used by industries.…”

Section: Introductionmentioning

confidence: 99%

“…The absorbance data of the bands in the spectra and the amount of the milk constituents, when associated with multivariate statistical analyses, produce accurate answers in the detection of adulteration, 12,17,18 enabling the development of mathematical models capable of predicting with reliable results the levels of tampering in the material under study. 19 A recent work 9 used MID study to verify the authenticity of buffalo's milk, experiments were performed with lyophilized milk samples and it was not possible to obtain a model capable of predicting the concentration of cow's milk in buffalo's milk, serving only as a screening technique. 9,12 For fluid milk, MID has not been observed so far to quantify the presence of cow's milk in buffalo's milk samples.…”

Section: Introductionmentioning

confidence: 99%

Fast Verification of Buffalo’s Milk Authenticity by Mid-Infrared Spectroscopy, Analytical Measurements and Multivariate Calibration

Gonçalves

Silva

Jesus

et al. 2020

J. Braz. Chem. Soc.

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Mid-infrared spectroscopy (MID), chemical composition and physicochemical characteristics associated with chemometrics were used to rapidly detect and quantify the amount of cow's milk added in buffalo's milk. A total of 165 samples, divided into buffalo's milk, buffalo's milk added with cow's milk (10 to 90%) and cow's milk were evaluated to obtain fat, protein, lactose, total and defatted solids, urea, pH, acidity, cryoscopic index and band absorbances in the spectra associated with principal component analysis (PCA), multiple linear regression (MLR) and partial least squares regression (PLS). The treatments were separated into groups by PCA, allowing the classification of samples. MLR and PLS models were able to predict cow's milk contents in buffalo's milk. MID and results of the analytical measures studied when associated with chemometrics are efficient in the rapid quantitative detection of adulteration in buffalo's milk.

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Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles

Cited by 70 publications

References 35 publications

Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

The effect of native and polymerised whey protein isolate addition on surface and microstructural properties of processed cheeses and their meltability determined by Turbiscan

Fast Verification of Buffalo’s Milk Authenticity by Mid-Infrared Spectroscopy, Analytical Measurements and Multivariate Calibration

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