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PC1-PC2 biplot obtained by performing PCA on the IV-GC dataset. Class Out: circles; class In: diamonds
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In this work, FT-NIR spectroscopy was employed to determine iodine value (IV) and fatty acids (FA) content of pig fat samples, through the combined use of signal preprocessing, multivariate calibration, and variable selection methods. In particular, the main focus was on the use of variable selection methods, both in order to improve the predictive...
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Citations
... However, though the method provided automatic measurement the continuous flowing of reagent still generated large amounts of waste solution. Furthermore, nuclear magnetic resonance(NMR), near infrared(NIR), and Furior transform infrared(FT-IR) spectroscopic techniques have been proposed (Baeten & Aparicio, 2000;Che Man & Setiowaty, 1999;Foca et al., 2016;Hendl, 2001;Yang et al., 2005). The gas chromatographic method is an alternative method for determination of IN as per AOCS Cd1c-85 (Firestone, 1989). ...
A greener analytical method for determination of iodine number (IN) of oils is presented. As per the AOAC standard method, a large amount of solvent and reagent was used, and long incubation time was required. This research is aimed at using less amount of solvent and reagent, less sample weight, and shorten the analysis time by using the modified titrimetric AOAC standard method. The study showed that by reducing the sample size, the amount of reagent could be decreased to 1.00 mL and the reaction time of 1 min is enough for completion of the reaction. The amount of reagent used was at least 25 times less than that of the classical method. There was no significant difference at 95% confidence level between the results obtained by the proposed method and the standard method, and both results correlated well. The present method can be applied to edible oils commonly found in the market (iodine number range of 6.0 to 130).
... On the flip side, the application of this technique initially entails a price to pay, namely, the preliminary work on signals to "train" the system to recognize the relevant features in the sample. NIR spectra serve as a sort of fingerprints of the sample that need to be appropriately "interpreted"; this is done using chemometric methods for spectral analysis, which focus on eliminating irrelevant information and interferences and extracting information useful for solving the problem of interest (Burns and Ciurczak 2007;Ferrari et al. 2011;Foca et al. 2011Foca et al. , 2016Barbon et al. 2020). ...
Wine vinegar is produced through a two-phase fermentation of grape must: initially, yeast converts grape sugars into ethanol, and subsequently, acetobacteria oxidize ethanol into acetic acid. This process, spanning weeks when conducted by surface fermentation, requires constant monitoring of ethanol and total acidity levels. To enhance the quality and efficiency of process monitoring, vinegar production is shifting to faster, environmentally sustainable methods. Near-infrared (NIR) spectroscopy, recognized for its non-invasiveness and speed, is ideal for online implementation in process control. This study tracked dual fermentation in red grape must over an extended period, monitoring two different batches simultaneously to assess fermentation kinetics and reproducibility. Ethanol content and total acidity were analyzed in fermenting musts throughout the whole fermentation process using both classical laboratory analyses and FT-NIR spectroscopy. Principal Component Analysis (PCA) was used to explore the spectral dataset, then Partial Least Squares (PLS) was used to develop calibration models for predicting ethanol and acidity. The models calculated considering the entire spectral range were compared with those obtained for two narrower zones, where more cost-effective and easily miniaturizable sensors are available on the market. FT-NIR allowed to effectively determine ethanol content and acidity (R²Pred > 0.98), both over the entire range (12,500–4000 cm⁻¹, corresponding to 800–2500 nm) and in the 10,526–6060 cm⁻¹ (950–1650 nm) region. Although less satisfactory, still acceptable results were obtained in the 12,500–9346 cm⁻¹ (800–1070 nm) region (R²Pred > 0.81), confirming the potential for cost-effective devices in real-time fermentation monitoring.
... The intensity of the reflected light is measured by the receiver of an NIR spectrometer. Analysis of the reflection spectra of NIR radiation has been established as a widely-used convenient, cost-effective, quick, and intact method [1,[9][10][11][12][13][14][15][16]. ...
... The aim of this work is to formulate a procedure suitable for the estimation of sugars in grapes, focusing on the Thompson Seedless cultivar. Variable selection was used to predict the concentrations of a variety of compounds in several plant and animal tissues or in extracts from them [10][11][12][13][14][15][16]27]. However, a literature search in Scopus did not return any articles on the application of variable selection for the prediction of sugar or TSS content in intact grape berries. ...
Fourier-transform near infrared (FT-NIR) reflection spectra of intact berries of the grape variety Thompson seedless were used to predict total soluble solids (TSS) content. From an initial dataset, 12 subsets were considered by applying variable selection to extract the reflectance values at wavenumbers most correlated to the chemometrically measured TSS content. The datasets were processed by both multiple linear regression (MLR) and partial least squares (PLS) methods towards predicting the TSS content from the reflection values of each spectrum. Prediction accuracy was measured in terms of both the coefficient of determination R2 and the root mean squared error (RMSE). It was found that variable selection improved the prediction accuracy with both processing methods; values of R2 of up to 0.972 and 0.926 and RMSE of up to 0.306 and 0.472 were reported with MLR and PLS, respectively. The combination of variable selection and MLR displayed (a) higher accuracy when the spectra dataset variation was limited, (b) lower accuracy with datasets of large variation such as those with spectra from a variety of maturity stages, and (c) failed with more complex spectra sets such as those from different harvest years. The combination of variable selection and PLS has demonstrated reliable prediction results with various degrees of dataset complexity.
... The presence of relation (but no linear) in the IMF and FA could support the SVR performance. Regarding possible synergy of correlation and SVR outcomes, Foca et al. (2016) stated that successful models prediction in total fat and FA could be related to spectra family-specific features rather than a specific FA features. ...
This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial R-SVR kernel. Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS, and R-SVR with and without wavelength selection based on genetic algorithms (GA). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation (RPD) of 4.8.
... But noting that not all of the wavelengths have a high signalto-noise ratio, spectral features should be extracted to identify the informative wavelengths, and a calibration model should be established and optimized. In recent years, both theoretical evidence and experimental evidence show that variable selection is on critical demand to find out the spectral features corresponding to the target analyte component (e.g., the protein), so that the performance of the calibration model can be significantly improved [14,15]. ...
The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable’s population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.
... For what regards fatty acid composition, the interpretation of the collected information is again difficult due to the important differences among studies with respect to diet and feeding, final body weight and age, and fatness, all these factors affecting fatty acid composition of tissues. Although it is difficult to make comparisons due to the differences in rearing and management conditions, the collected data indicate that local pig breeds in general exhibit higher proportion of monounsaturated fatty acids (MUFA) and lower proportion of polyunsaturated fatty acids (PUFA) as compared to the fatty acid profiles generally reported in conventional pig breeds [27][28][29]. The proportion of MUFA is mainly above 50% and PUFA below 12-13% in fatty acid profiles of intramuscular and back fat for the breeds considered, although in that respect, some of the breeds are closer to the conventional ones ( Figure 6). ...
... On intact subcutaneous fat, interval PLSR (iPLSR) was used by Sørensen, et al. [59] to calibrate a NIRS for IV prediction in unmelted fat obtaining high values of coefficient of determination. Foca, et al. [60] also considered the importance of variable selection in the perspective of developing robust models necessary for predicting the IV and FAs of intact pork fat. By the combination of signal processing and iPLSR, a two-step variable selection was applied to select the most informative variables that provide the most correlation with the NIR spectral data. ...
Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.
... The performance of the obtained calibration models was expressed in terms of the coefficient of determination, R 2 [40][41][42], calculated in calibration (R 2 Cal ), in cross-validation (R 2 CV ), and in prediction (R 2 Pred ). R 2 is particularly useful to compare directly models calculated on different response variables, since it does not depend on the scale of the dependent variable, y, and is defined by the following equation ...
An electronic tongue (ET) consisting of two voltammetric sensors, namely a poly-ethylendioxythiophene modified Pt electrode and a sonogel carbon electrode, has been developed aiming at monitoring grape ripening. To test the effectiveness of device and measurement procedures developed, samples of three varieties of grapes have been collected from veraison to harvest of the mature grape bunches. The derived musts have been then submitted to electrochemical investigation using Differential Pulse Voltammetry technique. At the same time, quantitative determination of specific analytical parameters for the evaluation of technological and phenolic maturity of each sample has been performed by means of conventional analytical techniques. After a preliminary inspection by principal component analysis, calibration models were calculated both by partial least squares (PLS) on the whole signals and by the interval partial least squares (iPLS) variable selection algorithm, in order to estimate physico-chemical parameters. Calibration models have been obtained both considering separately the signals of each sensor of the ET, and by proper fusion of the voltammetric data selected from the two sensors by iPLS. The latter procedure allowed us to check the possible complementarity of the information brought by the different electrodes. Good predictive models have been obtained for estimation of pH, total acidity, sugar content, and anthocyanins content. The application of the ET for fast evaluation of grape ripening and of most suitable harvesting time is proposed.
... In order to improve performance and robustness of the PLS calibration models (Xiaobo et al. 2010;Zeaiter et al. 2005) by keeping only those NIR spectral regions that are actually relevant for the prediction of the sugar content, a two-step variable selection procedure was used (Foca et al. 2016). ...
Thirty-nine samples of plant milks (rice, soy, oat, barley, spelt, quinoa, almond, and a variety of wheat called kamut) were analyzed for their reducing sugars content by NIR spectroscopy, using the Luff-Schoorl official method as reference to build the calibration models. The amount of reducing sugars, expressed as grams of glucose/100 mL of beverage, ranged from 0.5 g/100 mL (soy) to 7.6 g/100 mL (rice). Both partial least squares (PLS) and interval-partial least squares regression (iPLS) were used to build multivariate calibration models, testing different spectra preprocessing methods. The performance in prediction of the best calibration model was evaluated on an external test set of nine randomly selected samples (RMSEP = 0.98 g/100 mL, R2PRED = 0.84), and its statistical significance was assessed using a randomization t test based on Monte Carlo simulations. The results obtained suggest that NIR spectroscopy can be a valid alternative to the laborious reference titrimetric method for the determination of total glucose content in plant milks.
... The methods for evaluating the quality of fat are manifold, ranging from subjective methods (Wood, 1984), usually considered unreliable (Enser, 1983), to objective methods, primarily aimed to determine or estimate the fatty acid composition and texture of fat tissue (Foca et al., 2013;Foca et al., 2016;García-Olmo et al., 2002;Olsen, Baustad, Egelandsdal, Rukke, & Isaksson, 2010;Pérez-Marín, De Pedro Sanz, Guerrero-Ginel, & Garrido-Varo, 2009;Seman, Barron, & Matzinger, 2013;Zudaire & Alfonso, 2013). ...
In this work, different equations were compared as for their effectiveness in predicting the iodine value (IV), based on fatty acid (FA) composition of subcutaneous adipose tissue of Italian heavy pigs. In particular, six equations were tested: AOCS (1); modified AOCS (2), including all unsaturated FA (UFA); regression models obtained using the stepwise regression procedure as variable selection method, calculated considering only UFA (3) or all the FA (4); regression models obtained using the backward elimination procedure, calculated considering only UFA (5) or all the FA (6). The comparison of the equations performance, estimated using an external test set, showed that the use of regression models led to significant enhancements of prediction accuracy with respect to the AOCS equations. Using both equations 4 and 6, the average paired differences between experimental and predicted IV values were not statistically significant. Therefore, it is possible to use these equations for IV estimation of the subcutaneous adipose tissue of Italian heavy pigs.
