Journal of Spectral Imaging,   Volume 8   Article ID a15   (2019)

Peer reviewed Paper

An identification method for defective tablets by distribution analysis of near infrared imaging

  • Daitaro Ishikawa
  • Kodai Murayama
  • Takuma Genkawa
  • Yuma Kitagawa
  • Yukihiro Ozaki
Research and Development Department, Yokogawa Electric Corporation, 2-9-32 Nakacho, Musashino, Tokyo 180-8750, Japan

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Food Research Institute, NARO, 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan

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Research and Development Department, Yokogawa Electric Corporation, 2-9-32 Nakacho, Musashino, Tokyo 180-8750, Japan

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School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan

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 Corresponding Author
Faculty of Agricultural Sciences, Fukushima University, 1 Kanayagawa, Fukushima 960-1296, Japan
[email protected]
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The present study aims to suggest a method to identify defective tablets by near infrared (NIR) imaging. A newly developed portable imaging system (D-NIRs) was used in this study, in which the spectrometer is equipped with a high-density photodiode array detector to record high-quality spectra with 1.25 nm spectral resolution. This system is highly portable and allows an image of a target tablet to be developed in approximately 10 s. Normal tablets containing 0.1–20 % magnesium stearate, ascorbic acid, corn starch and talc were prepared. NIR spectra in the 950–1700 nm region of each pixel in a tablet were measured, and NIR images were generated from the second derivative of the spectra at 1213 nm. It was confirmed that the spectral distribution in a tablet passed as a normal distribution by the goodness-of-fit test (p ≤ 0.05). Consequently, the average of the spectra obtained from each pixel of the whole tablet was used to predict the concentration of magnesium stearate. The quantitative accuracy of the prediction model by the second derivative spectra achieved R2 = 0.931 and RMSE = 1.90 %. Defective tablets were prepared with localised magnesium stearate. The skewness of the second derivative in the defective tablet was larger than that of the standard distribution. Specifically, the distribution of defective tablets was biased to the right as compared to the standard distribution. The results of the presented study suggest that spectral imaging combined with distribution analysis is an effective method to identify defective tablets.

Keywords: NIR spectroscopy, defective tablet, distribution analysis, skewness, NIR imaging

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