Transformations Affecting Product Quality

Transformations Affecting Product Quality Drying, as with any other unit operation, has both productive and harmful transformations that occur. The primary productive transformation is water removal of course, but there are many harmful transformations that can occur and adversely affect product quality. The most common of these harmful transformations includes product shrinkage attrition or agglomeration loss of flavor, aroma, and nutritional value browning reactions discoloration stickiness and flowability problems. These were discussed briefly above, but are worth a more in-depth review. 

The above-mentioned on-line spectroscopic techniques have involved granulation monitoring for particle size assessments and end-point control however there are other concerns involved with wet granulation which include polymorphic transformations or solvate formation that can affect end product quality. ... 

Another important aspect in chemicals assessment, besides the hazard-based Pov and LRTP criteria, is how a chemical and its transformation products affect water quality. Water quality with respect to both human and ecosystem health is mostly assessed in a risk-based manner, i.e., measured or predicted environmental concentrations are compared to toxicity thresholds. When it comes to assessing the role of transformation products for water quahty, it is therefore crucial to be able to predict or measure the concentrations of the transformation products relative to each other and relative to the parent compounds. 

In production and storage of ammonium nitrate, transformations of the crystal states that may affect the quality of the product occur. Crystalline states are given in Table 8.12 121,22], Critical relative humidities [23] at increasing temperatures are given in Table 8.13. 

Crystallization is an important separation process that purifies fluids by forming solids. Crystallization is also a particle formation process by which molecules in solution or vapour are transformed into a solid phase of regular lattice structure, which is reflected on the external faces. Crystallization may be further described as a self-assembly molecular building process. Crystallographic and molecular factors are thus very important in affecting the shape (habit), purity and structure of crystals, as considered in detail by, for example, Mullin (2001) and Myerson (1999). In this chapter the internal crystal structure and external particle characteristics of size and shape are considered, which are important indicators of product quality and can affect downstream proeessing, such as solid-liquid separation markedly. Larger particles separate out from fluids more quickly than fines and are less prone to dust formation whilst smaller particles dissolve more rapidly. 

From the snap, gloss and texture of chocolate to the shelf life of frozen foods, crystalline microstructure plays a very important role in the texture, appearance, shelf life and overall quality of many foods. The total amount of crystaUine phase in a food, as well as the size distribution and shape of the crystals within the food, can affect the physical properties of the product. Furthermore, some mataials in food can crystallize in different polymorphic forms so that control of polymorphic transformations may also be necessary. 

These transformations are executed by using so-called kernel functions. The kernel functions can be both linear and nonlinear in nature. The most commonly used kernel function is of the latter type and called the radial basis function (RBF). There are a number of parameters, for example, cost functions and various kernel settings, within the SVM applications that will affect the statistical quality of the derived SVM models. Optimization of those variables may prove to be productive in deriving models with improved performance [97]. The original SVM protocol was designed to separate two classes but has later been extended to also handle multiple classes and continuous data [80]. 

Manufacturing processes are transformations that ideally provide perfectly predictable and stable results, but in practice are affected by variability. If a key quality characteristic (e.g., the diameter of a hole) is observed in multiple processed items, its value will not be identical, but will be a random variable following a statistical distribution. High variability in production processes leads to quality defects, lack of stability, and product inconsistency. Two different causes for variability can be defined in manufacturing processes, namely, random and assignable causes. 

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