Infrared Spectroscopic Identification of β-Thalassemia

Abstract

Background: The aim of this study was to investigate the potential of infrared (IR) spectroscopy as a fast and reagent-free adjunct tool in the diagnosis and screening of ␤-thalassemia. Methods: Blood was obtained from 56 patients with ␤-thalassemia major, 1 patient with hemoglobin H disease, and 35 age-matched controls. Hemolysates of blood samples were centrifuged to remove stroma. IR absorption spectra were recorded for duplicate films dried from 5 L of hemolysate. Differentiation between the two groups of hemoglobin spectra was by two statistical methods: an unsupervised cluster analysis and a supervised linear discriminant analysis (LDA). Results: The IR spectra revealed changes in the secondary structure of hemoglobin from ␤-thalassemia patients compared with that from controls, in particular, a decreased ␣-helix content, an increased content of parallel and antiparallel ␤-sheets, and changes in the tyrosine ring absorption band. The hemoglobin from ␤-thalassemia patients also showed an increase in the intensity of the IR bands from the cysteine ؊SH groups. The unsupervised cluster analysis, statistically separating spectra into different groups according to subtle IR spectral differences, allowed separation of control hemoglobin from ␤-thalassemia hemoglobin spectra, based mainly on differences in protein secondary structure. The supervised LDA method provided 100% classification accuracy for the training set and 98% accuracy for the validation set in partitioning control and ␤-thalassemia samples. Conclusion: IR spectroscopy holds promise in the clinical diagnosis and screening of ␤-thalassemia.

The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum

“…Normalisation is important for dissimilarity or distance measurement such as the Euclidean distance which is sensitive to the differences within the magnitude or scales of the variables [22]. It also helps to compensate for the differences in sample quantity or a different optical pathlength and improves accuracy and efficiency of the data models [23][24][25].…”

An Update on Novel Non‐Invasive Approaches for Periodontal Diagnosis

“…39 In the previous 10 years, IR spectroscopists took advantage of this molecular information, in combination with pattern recognition/classification methods, to explore the potential of IR spectroscopy as a powerful tool for the diagnoses of various diseases based upon the spectra of biologic fluids, including gingival crevicular fluid (GCF). [40][41][42][43][44][45] The IR spectrum of GCF is a rich source of information regarding the oral cavity and associated inflammation. …”

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

“…These bands are due to the presence of residual protons in the D 2 O buffer and the formation of HOD. A spectrum of this rare molecular species can be obtained by a second subtraction involving two D 2 O buffer spectra varying slightly by H content (see trace (d) of figure 4; a varying H content of D 2 O buffers can be attained by exposing D 2 O to the open atmosphere for a short time). Correction for spectral contributions of HOD in the amide II' region (N-D bending vibration coupled to C-N stretching) at ~1448 cm -1 ) can be achieved by a third subtraction operation between the difference spectra (c) and (d).…”

“…Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy.…”

“…In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3.…”