Soyflakes and soybrokens having 8% to 16% wet basis (w.b.) moisture contents were extracted for 8 h (about 50% extraction) using the azeotrope (91%) of isopropyl alcohol (IPA) at 7.75 ml/min flow rate. The moisture contents of soyflakes and soybrokens significantly affected oil recovery with IPA. Regression analysis was performed to optimize moisture contents of soyflakes and soybrokens during IPA extraction. The optimum moisture content was found to be 13.4% and 12.6% (w.b.) for soyflakes and soybrokens, respectively. Qualities of IPA-extracted oil [color and free fatty acid (FFA) content] and of de-oiled soy meal (whiteness value and crude protein content) were determined and compared with those of absolute n-hexane-extracted oil and meal at 4.15, 6.35, and 7.15 ml/min flow rates. Prior to the IPA and n-hexane extractions, soyflakes and soybrokens were hydrated to the optimum moisture content. The colors of IPA-extracted oil and soymeal were somewhat darker than those extracted by n-hexane. IPA-extracted oil had significantly lower FFA content than the n-hexane-extracted oil. De-oiled soy meals obtained from IPA extraction had lower whiteness values indicating darker color compared to nhexane. Crude protein contents were similar in both oil and meal obtained from both solvent extraction processes.
Advances in spectroscopy now enable researchers to obtain information about chemical and physical components in food or biological materials at the molecular level. Various spectroscopic techniques (e.g., atomic absorption spectroscopy, Raman and Fouriertransform infrared spectroscopy, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, mass spectroscopy, X-ray fluorescence spectroscopy, ultra-violet spectroscopy) have been used to study structure-function relationships in foods (both liquid and solid) to improve overall food quality, safety and sensory characteristics; to investigate fungal infections in plant materials (e.g., fruits, seeds); or to study mobility of different chemical components in food materials. Processing, analyzing, and displaying these data can often be difficult, time-consuming, and problem-specific. Chemometrics is well established for calibrating the spectral data to predict concentrations of constituents of interest. Similarly, proteomics deals with the structure-function relationship of proteins. Since most of the food processing industries are becoming increasingly automated, there is a need to understand how the spectroscopic data can be used for automation. In this paper, we have provided basic working principles of the above mentioned spectroscopic techniques, examples of the use of spectral data in food processing, methods of analysis of spectral data and their integration in the automation process.
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