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Chemical Analysis of Food: Techniques and Applications by Y Pico

By Y Pico

Chemical research of nutrients: strategies and purposes experiences new know-how and demanding situations in nutrients research from a number of views: a assessment of novel applied sciences getting used in foodstuff research, an in-depth research of a number of particular techniques, and an exam of the main leading edge purposes and destiny developments. The publication is based in components: the 1st describes the function of the most recent developments Read more...

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ANALYTICAL TECHNIQUES 42 2. 9 Example of k-NN class delimiter for k ¼ 1 (artificial data). closely related to k-NN, which defines the class space on the basis of a critical distance from the objects of the training set (see Fig. 10). Several settings can be varied, such as the type of distance (Euclidean or Mahalanobis), and the strategies adopted to determine the critical distance value. Unfortunately, this promising class-modeling technique has not been used anymore, and it would merit a thorough reconsideration.

They may affect the signal with baseline shifts and/or drifts, which can be considered as a low-frequency contribution. Signal processing is generally aimed at minimizing the unwanted variations, thus improving the quality of signals and, consequently, the conversion of data to valuable information. In particular, it is possible to individuate three main objectives: reduction of random noise, reduction of systematic unwanted variations, and reduction of data size. Several pre-processing techniques accomplish this with more than one point.

1. 2. Normality Tests Assessing for compatibility with a normal distribution is a basic issue in data analysis, because many methods require variables to be normally distributed. As observed, frequency distributions may be employed for this purpose. Visual examination of histogram shapes may supply a preliminary evaluation. Besides, the cumulative empirical frequency distributions (EFDs) constitute the basis for a family of statistical normality tests, which are usually referred to as KolmogoroveSmirnov tests (Kolmogorov, 1933; Smirnov, 1939).

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