Abstract
The moving-window bis-correlation coefficients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and [Formula: see text]-thalassemia with visible and near-infrared (Vis–NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG) smoothing with first-order derivative ([Formula: see text]), third-degree polynomial ([Formula: see text]) and 25 smoothing points ([Formula: see text]). The selected waveband was 736–1054[Formula: see text]nm with MW-BiCC, and the positive and negative validation recognition rates ([Formula: see text]_REC[Formula: see text], [Formula: see text]_REC[Formula: see text] were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 [Formula: see text]-thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with [Formula: see text], [Formula: see text] and [Formula: see text]. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342[Formula: see text]nm). The [Formula: see text]_REC[Formula: see text] and [Formula: see text]_REC[Formula: see text] were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing [Formula: see text]-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.
Highlights
Near-infrared (NIR) spectroscopy as a simple and quick tool has been e®ectively utilized in variouselds for quantitative and qualitative analysis, such as agriculture,[1,2,3,4,5,6] food,[7,8,9] environment,[10,11] biomedicine,[12,13,14,15,16] petroleum industry,[17] and so on.[18]
Pattern recognition technology based on NIR spectral information is presently an important research area,[18] such as distinction of di®erent melon genotypes,[2] identication of transgenic sugarcane leaves,[9] and classication of multiple online petroleum industrial products.[17]
The results showed that the selected MWBiCC model achieved the same validation e®ect
Summary
Near-infrared (NIR) spectroscopy as a simple and quick tool has been e®ectively utilized in variouselds for quantitative and qualitative analysis, such as agriculture,[1,2,3,4,5,6] food,[7,8,9] environment,[10,11] biomedicine,[12,13,14,15,16] petroleum industry,[17] and so on.[18]. Principal component analysis linear discriminant analysis (PCA–LDA) is the commonly well-performed method for spectral discriminant analysis.[2,9,17,18] The extraction of feature information and dimension reduction were performed based on a spectral data matrix that corresponds to variables (e.g., wavelengths) and samples. The original data matrix for the entire scan range is directly subjected to PCA–LDA. Wavelength selection is required in the sense of mathematics, physics and chemistry
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