Food Colors

Colour is the first attribute subject to consumer perception in determining food quality and, in many cases, this is the only possible mean to qualify product at purchase. For this reason, the description of colour by analytical methods is fundamental in food processing control. Computer vision systems acquire RGB data which are device-dependent and sensitive to the different lightning. Therefore, they are not directly useful for colour evaluation to mimic human vision. On the contrary, traditional colorimeters, which adopt CIELab coordinates, work in human-oriented colour space where euclidean distance between two different colours (∆E) is well related to the difference perceived by human sight. Nevertheless, vision systems have many advantages as the capability of acquiring larger areas of the food surface and the easiness of implementation in automated plants at low costs. Neural networks, trained on a set of selected colour samples, can approximate RGB to L*a*b* relationships to characterise the colour of food samples under test. The aim of this paper is to present a rapid method based on neural networks for the calibration of a CCD (Charge-Coupled Device) camera colour acquisition system to obtain reliable L*a* b* information. Preliminary results concerning the influence of the composition of the training test and the camera settings (aperture and time of exposure) on the reliability and accuracy of the colour measurement system are also discussed.

Samples from 67 manufactured lots of FD&C Yellow No. 6 (Sunset Yellow FCF; Colour Index No. 15985) were analysed for combined benzidine. These samples were selected from those submitted to the US Food and Drug Administration for certification between October 1991 and December 1992 by 13 dye distributors. Dithionite was used to reduce any combined benzidine present in the form of azo or disazo dyes to free benzidine. This reduction was followed by extraction, diazotization and coupling with 2-naphthol-3,6-disulfonic acid disodium salt (R salt). The total benzidine was quantified as benzidine-R salt disazo dye by HPLC with detection at 540 nm and a quantification limit of 10 ng benzidine/g FD&C Yellow No. 6. Of the 67 samples analysed, 34 were found to contain more than 10 ng combined benzidine/g. Of these, 30 samples were from one manufacturing company, including three of its subsidaries. The level of combined benzidine found ranged from 11 to 104 ng/g, except for one sample containing 941 ng/g.

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In the modern jungle of burgers, couches, and remote controls, obesity is an enormous and growing epidemic. Weight-loss books and diet gurus urge us to "listen to our bodies," but our instincts are designed for the African savannah, not food courts. The sugary and fatty foods that we, as hunter-gatherers, are programmed to forage used to be hard to come by. Now they're as close as the vending machine down the hall.

Radical changes are necessary and, fortunately, are biologically easier than small or gradual changes in diet. Barrett tells us how to reprogram our bodies, break food addictions, and ignore our attraction to "supernormal stimuli"—artificial creations that appeal to our instincts more than the natural objects they mimic. Barrett delves into scientific research—from animal ethology to evolution—to show the disastrous direction in which our instincts have led us, and how, using our intellect, we can get back on course.  . . . . . . . .An elegantly written and eye-opening analysis of what makes us fat. -- Steven Pinker, PhD, Johnstone Professor of Psychology, Harvard University, and author of How the Mind Works
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In this cogent, clearly argued, and thought-provoking book, Barrett shows how we are paying the price for our own plenty. -- David Spiegel, M.D. Medical Director, Center for Integrative Medicine, Stanford University School of Medicine; author of Living Beyond Limits
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Offers us a way to understand the pandemic of obesity that we find ourselves in. -- John Ratey, M.D. Clinical Associate Professor of Psychiatry Harvard Medical School, co-author of Driven to Distraction