Experimental results are given which demonstrate the sequential detection of single molecules with a measurement efficiency of near unity. IR140 dye molecules are detected in sequential probe volumes within a flowing stream through a 9 μm i.d. capillary. The measurement of single molecules was confirmed by means of autocorrelation, photobleaching, visual observation, and cross-correlation analysis. The number of single molecules photobleached prior to being measured in the second probe region was in excellent agreement with the bulk studies described by a photodestruction curve. A cross-correlation peak with a temporal delay corresponding to the interprobe volume transit time and a width in agreement with parabolic flow give a clear indication of sequential detection. The near unity measurement efficiencies for both channels indicates the great potential for rapid quantitative analysis of dilute solutions.

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With the demands on improved pharmaceutical productivity and quality, as well as the regulatory encouragement on applying process analytical technology (PAT), PAT is becoming increasingly recognized and embraced by pharmaceutical companies in both research and manufacturing areas. In this article, benefits of popular PAT tools, FTIR and Raman, to process development and scale‐up of active pharmaceutical ingredient (API) have been demonstrated. Applications of FTIR for in‐line monitoring and control of hazardous reactions, such as hydrogenation, Grignard reaction, and reaction involving potent species, are illustrated to achieve robust and safe processes. Meanwhile, in‐line measurement of a solute concentration by FTIR spectroscopy has made it possible to realize the feedback control of a crystallization process. In addition, for a better understanding and engineering of crystallization processes, Raman spectroscopy has shown as a valuable asset to characterize and quantify polymorphs in crystallization processes both in situ and at real‐time.

The sections in this article are Introduction – General Requirements Contrast to Laboratory Instruments Operating Performance Signal‐to‐Noise Ratio Instrument‐to‐Instrument Variability Long‐Term Drift Mean‐Time‐to‐Failure and Mean‐Time‐to‐Repair Calibration Transfer Design Considerations Analyzer Layout Modularity Backward/Forward Compatibility Environmental Influence User Interface Remote Connectivity I / O to Operations Troubleshooting and Diagnostics Power Requirements Reliability Maintenance and Repair Safety‐Regulatory Compliance Electrical Classification American Society of Mechanical Engineers Probe Certification Documentation Training On‐Line Quantitative Procedures FT ‐ IR Transmission Cells Attenuated Total Reflection Sample Systems and Analyzer Shelters Detectors FT ‐ NIR Probes Probe Installations Fibers and Connectors Fibers Connectors Multiplexers Sample Cells Detectors Applications When is the Use of FT ‐ IR or FT ‐ NIR Appropriate for On‐Line Analysis? Implementation Examples of Applications of FT ‐ IR and FT ‐ NIR Impurities in Anhydrous HCl by FT ‐ IR Analysis of Caustic Soda by FT ‐ NIR Attempted Analysis of Aqueous HCl /Aniline Solutions by FT ‐ NIR

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.