Predicting shelf life

What methodology and experimental scheme could be used for predicting shelf life ... 

Antibody stability in solution cannot be predicted without thorough stability studies technicians are advised to follow proper quality control procedures for stability validation if primary antibodies are to be diluted in the laboratory and utilized for extended periods of time. An advantage to using commercially diluted primary antibodies is the built-in customer protection provided by the regulatory mandates that govern reagent manufacturers. Manufacturers must demonstrate the stability of commercially produced reagents for defined periods to establish a predictable shelf life for their antibody products. 

The current approach to analyzing stability data to predict shelf-life involves statistical analysis of long-term... 

According to Lenz and Lund (72), kinetic models for destruction of food components are needed to improve products by minimizing quality changes for new product development and to predict shelf life during storage. Numerous reports and reviews of the kinetics of ascorbic acid destruction can be found in the literature (68-88). A brief overview is presented here to indicate the need for further research in this area. 

This useful equation may be used to predict the reaction rate at any temperature once kt and E are known for temperature Tt. This type of calculation is extremely important in pharmaceutical science since it is used to predict shelf-life for medicines. Once a medicine has been manufactured, it is stored under high-stress conditions (e.g. at elevated temperature, high humidity, under strong lighting, etc.), the rates of decomposition are measured and the activation energy is calculated. From these data, the value of k may be predicted and the likely shelf-life for the medicine can be calculated for room temperature (25°C) or refrigerator temperature (4°C). Another useful point to notice is that since k enters into the graphs as In k, and into the equations as a ratio, any physical quantity that is proportional to k, such as the actual reaction rates at fixed concentrations of reactants, may be used in the equation instead of k. 

To illustrate the application of the Q-Rule, let us assume that the stability of a product at 50°C is 32 days. The recommended storage temperature is 25°C and M = (50 - 25)/10 = 2.5. Let us set an intermediate value of Q = 3. Thus, Qn = (3)2.5 = 15.6. The predicted shelf life is 32 days x 15.6 = 500 days. This approach is more conservative when lower values of Q are used. Both Q-Rule and the bracket methods are rough approximations of stability. They can be effectively used to plan elevated temperature levels and the duration of testing in the accelerated stability testing protocol. 

Nelson, K.A. and Labuza, T.P. Water activity and food polymer science implications of state on Arrhenius and WLF models in predicting shelf-life, /. Food Eng., 22, 271,1994. 

The test substance was tested for identity, strength and purity before inclusion in the diet, and was given an analytical reference number. The test article was used within its predicted shelf-life. 

However, before moving on to these aspects, the converse of the above examples of risk could be counter-balanced by asking how realistic is your predicted shelf life as identified by initial investigative tests. 

Chapter 6 provides a thorough discussion of several factors that may impact the chemical stability of the API in its dosage form. Understanding these factors would help one to predict shelf-life of pharmaceutical products. 

From this, the predicted shelf life at the desired storage temperature can be calculated. 

There are several objectives for in house quality control. Microbial testing of raw materials, intermediate, and finished products allows the effectiveness of microbial control during processing to be assessed, whilst another major consideration of quality control is the predicted shelf life of the product. Quality control requirements, i.e. what to test for, how frequently to test, and what is acceptable, are usually management decisions based on experience, and are not mandatory requirements applied by an outside body. ... 

Actual (and Predicted) Shelf Life (Days) of Dried Onion Flakes Based on Browning and Thiosulfinate Loss at Different Temperatures... 

John Donohue and Spiro Apostolou (1998) offer more complex and advanced techniques for predicting shelf life of medical devices. Their contention is that the Arrhenius and gio techniques are... 

Donohue, J., and Apostolou, S., Predicting Shelf Life from Accelerated Aging Data The D A and Variable QIO Techniques, Medical Device and Diagnostic Industry, June 1998, pp. 68-72. 

Nuts and seed offer the widest choice of oils where the physical condition of the oil is important. Nut oils vary from very solid coconut and palm oils to very liquid canola, safflower and sunflower oils. Thus confectioners, bakers, salters, ice-cream makers and snack food manufacturers have a choice of nut oils to fulfill their needs. A single manufacturer of cookies, wafers, tea cakes, sandwich fillings, fruit cakes, layer cakes and pancake mixes has a choice of oils that meet the particular needs. Nuts and oily seed having the highest oil content are least stable. Schley cultivar of pecans, with 68% oil, is less stable than cultivars containing less oil. The iodine value, which measures the degree of unsaturation of the oil is used commercially as a means of predicting shelf-life. The ratio of oleic to linoleic acids is also a measure of oil stability. 

Temperature is a crucial factor affecting the quality and safety of food products diuing both distribution and storage. The difficulty in controlling and monitoring the temperature history of food products makes it difficult to precisely predict shelf life. Time-temperature indicators (TTIs) provide a visual summary of a product s accumulated chill-chain history, recording the effects of both time and temperatme. 

Laboratory information management system (LIMS) A computerized database that allows the tracking of samples, their various attributes and their analytical test data. Using appropriate software, the data can be manipulated to facilitate the development process, for example, to predict shelf life. 

SPME-MS-MVA shelf-life prediction models were developed for reduced-fat milk samples of known shelf life. Mass intensity lists were determined for 84 samples of reduced-fat milk. Sixty-four of these samples were used to develop a PLS calibration model, and 20 samples (a Model Validation Subset ) were randomly selected from the set of 84 total samples to evaluate how well the PLS model for reduced-fat milk could predict shelf life. 

Table 2 compares actual shelf life (determined by sensory evaluation) to predicted shelf life for the 20 PLS Model Validation Subset samples for reduced-fat milk. On average, the SPME-MS-MVA PLS model for reduced-fat milk predicted the shelf life with an accuracy of 0.62 days, with a correlation coefficient of 0.9801 and a range of -0.7 to 2.8 days. 

Table 2 Actual and Predicted Shelf Life of Homogenized, Pasteurized Reduced-Fat Milk...

Figure 13 Plot of predicted shelf life (based on PLS modeling) versus actual shelf life (based on sensory testing) for 64 samples used to prepare a PLS model for reduced-fat milk. Analyses performed by SPME-MS-MVA.

PREDICTING SHELF LIFE OF MEDICAL GRADE POLYMERS USING... 

Damage can be noticed when melt flow index tests are performed and the reduction in molecular weight causes a higher melt flow value. Also, damage can be noticed when tensile tests are performed and decreases in ultimate elongation are noticed. The intention of this research is to derive a new Qio constant for the Arrhenius Equation to better predict shelf life of medical polymers. 

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