On-line prediction of cheese making indices using backscatter of near infrared light

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Effect of high hydrostatic pressure processing on the rennet coagulation kinetics and physicochemical properties of sheep milk rennet-induced gels

The effects of high hydrostatic pressure on the constituents and coagulation ability and their effect on cheese production of sheep milk have not been studied in detail. The objective of this work was to evaluate the effect of high hydrostatic pressure processing on the coagulation kinetics and physicochemical properties of sheep milk and to explore how such treatment could improve the cheesemaking process. Five batches of milk were tested: 1 untreated control batch and 4 batches each subjected to a different pressure (150, 300, 450, or 600 MPa) for 5 min at 10°C. As treatment pressure increased, values of electrical conductivity and oxidation-reduction potential were found to decrease. However, no significant reduction in pH was recorded. Treatment pressures >300 MPa produced milk with lower lightness (luminosity) and a more yellow and green hue. Pressures >150 MPa resulted in micellar fragmentation, as well as significant increases in particle size, viscosity, and water-holding capacity as a consequence of the denaturing of soluble proteins. High-pressure treatments increased the solubility of colloidal calcium phosphate, leading to a considerable increase in the concentration of minerals in the serum phase. The highest concentrations of calcium and phosphorus in the rennet whey of milk were reached at 300 MPa. Curd coagulation time was reduced by 28% at pressures >300 MPa, and an increase in the curd firming rate was observed. As treatment pressure increased to 450 MPa, the firmness, elasticity, and the percentage creep recovery of gels increased, whereas values of compliance and fracture strain were reduced. Thus, we can conclude that 300 MPa is the optimum treatment pressure for milk intended for cheesemaking by enzymatic coagulation. This pressure produced milk with optimal coagulation kinetics and water-holding properties with the least loss of fat and protein to the whey.

Applications of Photonics in Agriculture Sector: A Review

The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

Usefulness of a large field of view sensor for physicochemical, textural, and yield predictions under industrial goat cheese (Murcia al Vino) manufacturing conditions

The applicability of a light backscatter sensor with a large field of view was tested for on-line monitoring of coagulation and syneresis in a goat cheese (Murcia al Vino) manufactured under industrial conditions. Cheesemaking was carried out concurrently in a 12-L pilot vat and a 10,000-L industrial vat following the normal cheesemaking protocol. Cheese moisture, whey fat content, hardness, springiness, and adhesiveness were measured during syneresis. The results obtained show that cutting time is best predicted by considering the coagulation ratio at the inflection point and the percentage increase in the ratio during coagulation, with no need for the first derivative. The large field of view reflectance ratio provided good results for the prediction of moisture content, yield, hardness, springiness, and adhesiveness of the final cheese.

Preliminary evaluation of endogenous milk fluorophores as tracer molecules for curd syneresis

A front-face fluorescence spectroscopy probe was installed in the wall of a laboratory-scale cheese vat. Excitation and emission filters were chosen for the selective detection of vitamin A, tryptophan, and riboflavin fluorescence. The evolution of the fluorescence of each fluorophore during milk coagulation and syneresis was monitored to determine if they had the potential to act as intrinsic tracers of syneresis and also coagulation. The fluorescence profiles for 2 of the fluorophores during coagulation could be divided into 3 sections relating to enzymatic hydrolysis of κ-casein, aggregation of casein micelles, and crosslinking. A parameter relating to coagulation kinetics was derived from the tryptophan and riboflavin profiles but this was not possible for the vitamin A response. The study also indicated that tryptophan and riboflavin may act as tracer molecules for syneresis, but this was not shown for vitamin A. The evolution of tryptophan and riboflavin fluorescence during syneresis followed a first-order reaction and had strong relationships with curd moisture and whey total solids content (r=0.86-0.96). Simple 1- and 2-parameter models were developed to predict curd moisture content, curd yield, and whey total solids using parameters derived from the sensor profiles (standard error of prediction=0.0005-0.394%; R(2)=0.963-0.999). The results of this study highlight the potential of tryptophan and riboflavin to act as intrinsic tracer molecules for noninvasive inline monitoring of milk coagulation and curd syneresis. Further work is required to validate these findings under a wider range of processing conditions.