Light Backscatter of Milk Products for Transition Sensing Using Optical Fibers

Transition sensors are needed, particularly in the dairy industry, for detecting transitions in pipe flow systems from product-to-water or product-to-product (such as from chocolate to vanilla ice cream mix). Transition information is used to automatically sequence valves to minimize product waste. Optical fibers were used to measure light backscatter between 400 and 950 nm as a function of milk concentration in water and milkfat concentration in milk. The normalized response (100% for product and 0% for water) as a function of product concentration in water was approximately logarithmic for skim milk between 400 and 900 nm and approximately linear for milk containing 1, 2, and 3.2% milkfat. The backscatter ratio (response relative to that for skim milk) as a function of milkfat in milk was wavelength dependent with longer wavelengths being more sensitive. The backscatter ratio at 900 nm for milk containing 3.2% homogenized fat was nearly four times that for skim milk. Backscatter ratio saturated (minimal response with increased milkfat) at 8% milkfat for homogenized cream and 16% milkfat for unhomogenized cream. Light backscatter for near infrared wavelengths around 900 nm was found ideally suited for transition sensing of dairy products and was found particularly sensitive to milkfat content. Light backscatter was found less suitable for discriminating between high milkfat products

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats