Thyroid nodules are very common, and their incidence increase with age. The majority of thyroid nodules are benign, but approximately 5-15% are thyroid cancer. The cornerstone to evaluation of most thyroid nodules is a neck ultrasound followed by fine-needle aspiration (FNA) to evaluate for malignancy. Unfortunately, approximately 15-30% of FNAs are considered “indeterminate”. In these cases, cytopathologist cannot determine if the thyroid nodule is benign or malignant based on FNA alone. Although the majority of these “indeterminate” nodules are ultimately benign, majority of these patients require thyroidectomies to rule out cancer. This puts the patient at unnecessary risk of surgical complications and increases health care costs. A better method is needed to help physicians determine the risk of malignancy in patients with indeterminate thyroid nodules. In recent years there has been much interest in the use of optical diagnostic in cancer detection. Recent investigations potentially suggest that Raman spectroscopy (RS) can be used as a clinical tool that could confer great patient advantage with minimally invasive, non-destructive, rapid and accurate diagnosis. In this study, we investigate the use of line-scan RS and imaging in combination with multivariate statistical analysis of the spectral data for objective identification and classification of single cells isolated from frozen samples of different types of human thyroid nodules. Preliminary results indicate a high sensitivity and specificity for identifying different cell types.
Authenticity is an important food quality criterion. Rapid methods for confirming authenticity or detecting adulteration
are increasingly demanded by food processors and consumers. Near infrared (NIR) spectroscopy has been used to detect
economic adulteration in pork . Pork samples were adulterated with liver and chicken in 10% increments. Prediction and
quantitative analysis were done using raw data and pretreatment spectra. The optimal prediction result was achieved by
partial least aquares(PLS) regression with standard normal variate(SNV) pretreatment for pork adulterated with liver
samples, and the correlation coefficient(R value), the root mean square error of calibration(RMSEC) and the root mean
square error of prediction (RMSEP) were 0.97706, 0.0673 and 0.0732, respectively. The best model for pork meat
adulterated with chicken samples was obtained by PLS with the raw spectra, and the correlation coefficient(R value),
RMSEP and RMSEC were 0.98614, 0.0525, and 0.122, respectively. The result shows that NIR technology can be
successfully used to detect adulteration in pork meat adulterated with liver and chicken.
In this study, the application potential of computer vision in on-line determination of CIE L*a*b* and content of
intramuscular fat (IMF) of pork was evaluated. Images of pork chop from 211 pig carcasses were captured while samples
were on a conveyor belt at the speed of 0.25 m•s-1 to simulate the on-line environment. CIE L*a*b* and IMF content
were measured with colorimeter and chemical extractor as reference. The KSW algorithm combined with region
selection was employed in eliminating the surrounding fat of longissimus dorsi muscle (MLD). RGB values of the pork
were counted and five methods were applied for transforming RGB values to CIE L*a*b* values. The region growing
algorithm with multiple seed points was applied to mask out the IMF pixels within the intensity corrected images. The
performances of the proposed algorithms were verified by comparing the measured reference values and the quality
characteristics obtained by image processing. MLD region of six samples could not be identified using the KSW
algorithm. Intensity nonuniformity of pork surface in the image can be eliminated efficiently, and IMF region of three
corrected images failed to be extracted. Given considerable variety of color and complexity of the pork surface, CIE L*,
a* and b* color of MLD could be predicted with correlation coefficients of 0.84, 0.54 and 0.47 respectively, and IMF
content could be determined with a correlation coefficient more than 0.70. The study demonstrated that it is feasible to
evaluate CIE L*a*b* values and IMF content on-line using computer vision.
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