ABSTRACT
Instrumental techniques such a near-infrared spectroscopy (NIRS) are used in industry to monitor and establish product composition and quality. As occurs with other food industries, the Chilean flour industry needs simple, rapid techniques to objectively assess the origin of different products, which is often related to their quality. In this sense, NIRS has been used in combination with chemometric methods to predict the geographic origin of wheat grain and flour samples produced in different regions of Chile. Here, the spectral data obtained with NIRS were analysed using a supervised pattern recognition method, Discriminat Partial Least Squares (DPLS). The method correctly classified 76% of the wheat grain samples and between 90% and 96% of the flour samples according to their geographic origin. The results show that NIRS, together with chemometric methods, provides a rapid tool for the classification of wheat grain and flour samples according to their geographic origin.
Subject(s)
Flour/analysis , Food Quality , Seeds/chemistry , Triticum/chemistry , Artificial Intelligence , Chemistry, Agricultural/methods , Chile , Climate , Discriminant Analysis , Fiber Optic Technology , Food Inspection/methods , Least-Squares Analysis , Metabolomics/methods , Pattern Recognition, Automated , Quality Control , Reproducibility of Results , Seasons , Seeds/growth & development , Species Specificity , Spectroscopy, Near-Infrared , Triticum/growth & developmentABSTRACT
Two independent methodologies were investigated to achieve the differentiation of ewes' cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer and winter) and ripening times up to 9 months were elaborated. Their mineral composition or the information provided by their spectra in the near infrared zone (NIR) coupled to chemometric tools were used in order to differentiate between organic and non-organic cheeses. Main mineral composition (Ca, K, Mg, Na and P) of cheeses and stepwise lineal discriminant analysis were used to develop a discriminant model. The results from canonical standardised coefficients indicated that the most important mineral was Mg (1.725) followed by P (0.764) and K (0.742). The percentage of correctly classified samples was 88% in internal validation and 90% in external validation, selecting Mg, K and P as variables.Spectral information in the NIR zone was used coupled to a discriminant analysis based on a regression by partial least squares in order to obtain a model which allowed a rate of samples correctly classified of 97% in internal validation and 85% in external validation.
Subject(s)
Cheese/analysis , Minerals/chemistry , Organic Chemicals/analysis , Sheep , Spectrophotometry, Infrared/methods , Animals , Discriminant Analysis , Female , Least-Squares AnalysisABSTRACT
BACKGROUND: Owing to the importance of the season of collection of milk for cheese quality, a study was made of the usefulness of near-infrared spectroscopy (NIRS) for discriminating the seasonal origin (winter or summer) of milk and quantifying the fat content of cheeses, since fat is one of the components most affected by the season of collection of milk for the elaboration of cheeses. RESULTS: In the internal validation, 96% of samples from winter milk and 97% of samples from summer milk were correctly classified, while in the external validation the prediction rate of samples correctly classified was 92%. Moreover, quantitative models allowed the determination of fat in winter, summer and winter + summer cheeses. CONCLUSION: Rapid prediction of the fat content of cheeses and the seasonal origin (winter or summer) of milk was achieved using NIRS without previous destruction or treatment of samples.
Subject(s)
Cheese/analysis , Animals , Cattle , Cheese/classification , Dietary Fats/analysis , Fiber Optic Technology , Food-Processing Industry/methods , Goats , Models, Statistical , Quality Control , Seasons , Sheep, Domestic , Spectroscopy, Near-InfraredABSTRACT
In the present work the potential of near infra-red spectroscopy technology (NIRS) together with the use of a remote reflectance fibre-optic probe for the analysis of fat, moisture, protein and chlorides contents of commercial cheeses elaborated with mixtures of cow's, ewe's and goat's milk and with different curing times was examined. The probe was applied directly, with no previous sample treatment. The regression method employed was modified partial least squares (MPLS). The equations developed for the cheese samples afforded fat, moisture, protein, and chloride contents in the range 13-52%, 10-62%, 20-30%, and 0.7-2.9%, respectively. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained were respectively 0.97 and 0.995% for fat; 0.96% and 1.640% for moisture; 0.78% and 0.760% for protein, and 0.89% and 0.112% for chlorides.
Subject(s)
Cheese/analysis , Fiber Optic Technology , Spectroscopy, Near-Infrared/methods , CalibrationABSTRACT
In the present work we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of the mineral composition of animal feeds. The method allows immediate control of the feeds without prior sample treatment or destruction through direct application of the fibre-optic probe on the sample. The regression method employed was modified partial least squares (MPLS). The calibration results obtained using forty samples of animal feeds allowed the determination of Fe, Mn, Ca, Na, K, P, Zn and Cu, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) of 0.129 and 0.859 for Fe; 0.175 and 0.816 for Mn; 5.470 and 0.927 for Ca; 2.717 and 0.862 for Na; 4.397 and 0.891 for K; 2.226 and 0.881 for P; 0.153 and 0.764 for Zn, and 0.095 and 0.918 for Cu, respectively. The robustness of the method was checked by applying it to 10 animal feeds samples of unknown mineral composition in the external validation.
