Detection of capsule tampering by near-infrared reflectance analysis

Abstract : The growing incidence of product tampering has brought to attention the need for a rapid, reliable, inexpensive, noninvasive and nondestructive method of screening. Such a method, based on near-infrared reflectance analysis (NIRA), is presented here for the detection of adulterated non prescription drugs. The method relies upon a nonparametric clustering algorithm known as the BEAST (Bootstrap Error-Adjusted Single-sample Technique). Specially-designed sampling reflectors have been constructed to enable rapid and convenient measurement of capsules. A right-circular conical reflector has been found to be optimal for this purpose; the capsules fit directly into the reflector and need not be opened for analysis. A variety of foreign substances have been successfully detected in capsules by probing the capsule contents directly through the gelatin walls; these substances include Fe2O3, Al shavings, NaF, As2O3 NaCN and KCN. The NIRA response for KCN is linear down to a detection limit of 2.6 mg (0.4% of capsule weight). An incidental advantage in the use of the conical reflector is that the response is dependent not only upon the mass of adulterant but its location within the capsule. Keywords: Sensors, Near infrared reflectance analysis, Capsule tampering, Fault detection.

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