SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

Vasconcelos, Lia; Dias, Luís G.; Leite, Ana; Ferreira, Iasmin; Pereira, Ermelinda; Silva, Severiano; Rodrigues, Sandra; Teixeira, Alfredo

doi:10.3390/foods12030470

Cited by 8 publications

(9 citation statements)

References 40 publications

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“…Internal validation is a commonly used method for evaluating models free of experimental and environmental conditions’ limitations ( Luo et al, 2020 ; Vasconcelos et al, 2023 ). In this study, we used 10-fold cross validation to conduct internal validation.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

et al. 2023

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Introduction: Oligopeptides exhibit great prospects for clinical application and its separation is of great importance in new drug development.Methods: To accurately predict the retention of pentapeptides with analogous structures in chromatography, the retention times of 57 pentapeptide derivatives in seven buffers at three temperatures and four mobile phase compositions were measured via reversed-phase high-performance liquid chromatography. The parameters (kHA, kA, and pKa) of the acid–base equilibrium were obtained by fitting the data corresponding to a sigmoidal function. We then studied the dependence of these parameters on the temperature (T), organic modifier composition (φ, methanol volume fraction), and polarity (PmN parameter). Finally, we proposed two six-parameter models with (1) pH and T and (2) pH and φ or PmN as the independent variables. These models were validated for their prediction capacities by linearly fitting the predicted retention factor k-value and the experimental k-value.Results: The results showed that logkHA and logkA exhibited linear relationships with 1/T, φ or PmN for all pentapeptides, especially for the acid pentapeptides. In the model of pH and T, the correlation coefficient (R2) of the acid pentapeptides was 0.8603, suggesting a certain prediction capability of chromatographic retention. Moreover, in the model of pH and φ or PmN, the R2 values of the acid and neutral pentapeptides were greater than 0.93, and the average root mean squared error was approximately 0.3, indicating that the k-values could be effectively predicted.Discussion: In summary, the two six-parameter models were appropriate to characterize the chromatographic retention of amphoteric compounds, especially the acid or neutral pentapeptides, and could predict the chromatographic retention of pentapeptide compounds.

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Section: Resultsmentioning

confidence: 99%

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

et al. 2023

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“…55 Similar findings were made by another researcher, who concluded that SVMR outperformed PLSR in prediction performance. 45,46,55,56 In this study, the best prediction performance values are provided by MLPR, a nonparametric regression. Applying a nonparametric regression model based on a local polynomial estimator outperforms a mathematical computation technique, including the support vector machine approach.…”

Section: Tss Valuementioning

confidence: 99%

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling

Ulya,

Chamidah,

Saifudin

2024

F1000Res

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Background pH and total soluble solids (TSS) are important quality parameters of mangoes; they represent the acidity and sweetness of the fruit, respectively. This study predicts the pH and TSS of intact mangoes based on near-infrared (NIR) spectroscopy using multi-predictor local polynomial regression (MLPR) modeling. Herein, the prediction performance of kernel partial least square regression (KPLSR), support vector machine regression (SVMR), and MLPR is compared. Methods For this purpose, 186 intact mango samples at three different maturity stages are used. Prediction models are built using MLPR, KPLSR, and SVMR based on untreated and treated spectra. The best regression model for predicting pH is MLPR based on Gaussian filter smoothing spectra. Moreover, the TSS value is more accurately predicted using MLPR based on Savitzky–Golay smoothing. Results The findings reveal that MLPR is highly accurate in estimating the pH and TSS of mangoes, with mean absolute percentage error (MAPE) values less than 10 %. In addition, the MLPR model has the best predictive performance with the lowest Mean Squared error (MSE) and root mean squared error (RMSE) values and the highest R2 value. Conclusions The use of NIR spectroscopy in combination with multi-predictor local polynomial regression could provide a quick and non-destructive technique for predicting mango quality. Thus, the results of this study help support sustainable production as a sustainable development goal.

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“…Chewiness determines the number of chews required for the meat to be ready for swallowing, and juiciness is dependent on the protein structures/compositions of the muscle fibers and connective tissues and is correlated to the sensation of moisture observed in the initial chewing movements [44,47,48]. It was possible to verify that the intramuscular composition and fat deposition existing in the dry-cured Bísaro loin lines [22,26] resulted in the rapid release of fluid contained in them, justifying the variation found in the texture parameters of our work. All of them showed a relatively large range (6.56-2.44; 5.44-2.13; 6.11-3.44 for hardness, chewiness and juiciness, respectively) with average values of 3.90, 3.62 and 5.10, respectively, and standard deviations of 1.23, 0.85 and 0.64, respectively.…”

Section: Sensory Datamentioning

confidence: 99%

“…Regarding appearance, mean values for fat color and color were lower (3.11 and 4.05, respectively) than those described by Revilla et al [49] and were within those obtained by Seong et al [53], with a value of 4.75 given by tasters for loins ripened for 60 days in their study. The color of meat is closely associated with freshness, myoglobin content and technological quality traits such as pH or water-holding capacity, which, in Bísaro pork meat, indicate significant variations in color and tenderness [22,26,45]. These indicators play a crucial role in visual attraction and sensory acceptability and are directly related to financial losses for the industry if they do not meet consumer demands [11,13].…”

Section: Sensory Datamentioning

confidence: 99%

“…Alternative techniques are increasingly gaining priority due to prompt, easy-to-use and minimal pre-processing requirements, making them suitable for rapid implementation in industrial meat applications [12]. Most studies reveal the NIR spectra potential to provide real-time QC based on distinguishing quality labels; predicting the physico-chemical composition, technological parameters and sensory attributes; and classifying and identifying specific meat and meat products [16,[20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?

Vasconcelos,

Dias,

Leite

et al. 2023

Foods

Self Cite

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This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.

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SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

Cited by 8 publications

References 40 publications

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling

Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?

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