2000
DOI: 10.1002/1520-6017(200010)89:10<1305::aid-jps8>3.0.co;2-q
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Application of Diffuse Reflectance Near‐Infrared Spectroscopy for Determination of Crystallinity

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Cited by 56 publications
(16 citation statements)
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“…In addition, further quantitative information can be extracted from the data using chemoinfometrics, where the collected data is processed by means of statistical and applied mathematical techniques. Quantification of crystallinity is usually performed using the first (140) or second derivate spectra (95,141,142). Physical and chemical information such as polymorphism (143) and mutarotation (144) may be obtained.…”
Section: Near Infrared Spectroscopymentioning
confidence: 87%
“…In addition, further quantitative information can be extracted from the data using chemoinfometrics, where the collected data is processed by means of statistical and applied mathematical techniques. Quantification of crystallinity is usually performed using the first (140) or second derivate spectra (95,141,142). Physical and chemical information such as polymorphism (143) and mutarotation (144) may be obtained.…”
Section: Near Infrared Spectroscopymentioning
confidence: 87%
“…MLR predicted a crystallinity   of 0.99 for the mixtures of crystalline and amorphous lactose, which indicated that NIR can determine the crystallinity of materials during pharmaceutical manufacturing. Seyer et al (2000) conducted a study on the identification of the degree of crystallinity in powder mixtures of sucrose and indomethacin using diffuse reflectance NIR spectroscopy (1100-2500 nm). This study also used XRD and DSC methods for parallel studies of the mixtures.…”
Section: Polymorphismmentioning
confidence: 99%
“…Finally, a new model was generated from a combination of the original and restricted calibrations, leading to: R-SEC = 0.117% (w/w); calibration slope = 0.992; calibration intercept = 0.0174; R 2 Cal = 0.996; RMSEP = 0.233% (w/w); RSEP = 8.21% (w/w); prediction slope = 0.944; prediction intercept = 0.0147; R 2 Pred = 0.994. The limit of detection (LOD) and LOQ were estimated by calculating the standard deviation (SD) of samples from the calibration and validation sets with moisture content close to the LOD and LOQ (range was selected iteratively) and multiplying each SD by 3 and 10, respectively (Patel et al, 2001;Seyer et al, 2000). This estimation provides an approximate LOD value of 0.22% (w/w) and LOQ value of 0.73% (w/w).…”
Section: Model Assessmentmentioning
confidence: 99%