The potential of different spectroscopic techniques for evaluating potato (Solanum tuberosum L.) quality was investigated. Spectral data in the wavelength range of 400-1750 nm were used to develop quality prediction models. The Partial Least Squares (PLS) regression was used for predicting the water content in potato samples. Water content was predicted with R2 ≥ 0.938. / A further study was conducted to find the best wavelengths for predicting water content using two methods, PLS and multiple linear regression. Wavelength ranges of 910-1020, 1129-1211, 1363-1403 nm were selected for samples without skin, while 700-900, 930-1050, 1100-1300, 1400-1550 nm were selected for samples with-skin. Weight prediction models were established using the predicted water content. / Visible spectroscopy was used for classifying shriveled and non-shriveled potatoes. The wavelength ranges best suited to such a classification were those of 442-452, 456-466, 641-651, and 684-694 nm, with accuracies as high as 94.28% and as low as 80%.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 194-200). / Microfiche. / xiv, 200 leaves, bound ill. 29 cm

Thesis (MAppSc)--University of South Australia, 2000

Thesis (MAppSc)--University of South Australia, 2000

Thesis (M.S.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (January 11, 2007) Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 178-184). Available also in a digital version from Dissertation Abstracts.

Introduction: Process analytical technologies are systems for the analysis and control of manufacturing processes to assure acceptable end-product quality. This is achieved by timely measurements of critical parameters and performance attributes of raw material and in-process material and processes. The introduction of process analytical technology using near infrared analysis was investigated in three areas, namely incoming raw material analysis, blend uniformity analysis and moisture determination in the fluid bed dryer. Methodology: Incoming raw material identification - The FOSS XDS rapid content analyzer was used for the development of a NIR method for the identification and material qualification of starch maize and lactose monohydrate. Blend uniformity analysis – The SP15 Laboratory Blender fitted with near infrared probe was utilized for the study. Two types of blend experiments were designed to monitor the distribution of magnesium stearate (lubricant) in the blend, namely, a powder blend utilizing lactose monohydrate and a granule blend utilizing Ridaq® granule. Software methods were developed to monitor the standard deviation of the absorbance at the wavelengths that were specific for lactose monohydrate, Ridaq® granule and magnesium stearate. To confirm the prediction of end-point using near infrared, results were verified using an atomic absorption method for magnesium stearate. The blends were sampled at the selected time intervals corresponding to three states of the blend, namely, before end-point, at end-point and after end-point using a sampling plan. An additional six blends were conducted for the granule blend and sampled when the standard deviation had reached a value below 3 x 10-6 at the magnesium stearate wavelength at four consecutive data points (standard deviation value extrapolated from blends carried out to predetermined time intervals). Moisture determination in the fluid bed dryer – Moisture values for two products (Product A and Product B) were retrospectively collected from past production batches. A process capability study was conducted on the moisture values to determine if the current process was in a state of control. Results and Discussion: Incoming raw material identification – The algorithms used for the spectral library were able to distinguish between the raw materials selected. The spectral library positively identified the starch maize and lactose monohydrate samples that were not present in the library. The negative challenge with pregelatinised starch and tablettose demonstrated that the spectral library was able to differentiate between closely related compounds. Blend uniformity analysis – Blends sampled at the predetermined time intervals demonstrated a homogeneous state when the standard deviation of the absorbance was low and a non-homogeneous state when the standard deviation of the absorbance was high, thus near infrared prediction on the state of the blend was confirmed by the standard analytical methods. The series of Ridaq® granule and magnesium stearate blends sampled when the standard deviation was below 3 x 10-6 were homogeneous with the exception of one blend that was marginally out of specification. Blend durations were significantly lower than the standard blend durations used in the facility and ranged from 112 to 198 seconds. Moisture determination in the fluid bed dryer – From the process capability study of the two products it was noted that Product A is stable but can still be optimized while Product B is at a desirable state. The statistical evaluation of the moisture values for Product A and Product B demonstrated that the use of the product temperature to monitor the moisture gave consistent results. The current process is stable and capable of producing repeatable results although near infrared provides a means for continuously monitoring the product moisture and allows one to take action to prevent over-drying or under-drying. Conclusion: From the investigations conducted, it can be seen that there is definitely a niche for process analytical technology at this pharmaceutical company. The implementation is a gradual process of change, which may take time, probably several years (Heinze & Hansen 2005).

Over the last several years questions about the strength of structural lumber have been raised. The purpose of this study was to examine the relationship between physically measured wood properties using Near Infrared spectroscopy. Physical properties were determined from (2 x 4 x 2 in.) cut samples. Destructive mechanical testing was performed on 744; 8 feet long, No.2 grade 2x4’s. Diffuse reflectance NIR spectra was collected from the cross-sectional face of each block using FOSS NIR Systems Inc. Model 5000 scanning spectrometer. Calibrations were then created between measured properties and NIR estimates. Density, specific gravity, latewood percentage, MOE and MOR had coefficient of determinations of 0.78, 0.56, 0.02, 0.56, and 0.48 respectively. The low correlation is likely caused by the grade of lumber. Because No.2 lumber has considerable knots, they were the determining factor in strength and stiffness; these results would likely not be similar in a higher grade lumber.

No description available.

published_or_final_version / Sports Science / Master / Master of Science in Sports Science