A method of determining information indicative of a material attribute of a composition, wherein the method comprises manufacturing the composition using a thermal manufacturing process, detecting detection data from the composition by low coherence interferometry during the manufacturing, in particular during said thermal manufacturing process, and determining the information based on the detected detection data.

Disclosed are methods for quantifying the cleanability of a surface of an article, for instance an aircraft interior component. In embodiments, a solution containing an artificial “dirt” is introduced to a surface under test to simulate a sullied condition. The sullied surface is subjected to at least one cleaning process. Obtained reference and post-cleaning light measurements are compared to determine a measurement difference corresponding to a cleanability of the surface and/or effectiveness of the at least one cleaning process. In embodiments, the measurement difference is assigned a cleanability score for at least one of determining a passing or failing cleanability, modifying the cleaning process, indicating the need for a second or subsequent cleaning, modifying the barrier coating formulation, redesigning the article, etc. The disclosed methods provide solutions for objective verification measures of surface cleanability used to facilitate new article designs, materials, barrier coatings, cleaning processes, cleaners, etc.

A method of characterizing and monitoring a pressing process includes acquiring online Raman spectra of a juice pressing process within a vessel at different times during the pressing process to generate a training data set; acquiring physical samples from pressing process near in time to the acquired Raman spectra; performing offline measurements of the target analyte properties and/or compositions using an assay measurement technique; generating a correlative model of the target analyte such that spectral changes in the training data set correlate with the offline measurements of the target analyte properties and/or compositions; acquiring online Raman spectra of a subsequent run of the pressing process within the vessel at different times during the run to generate a process data set; and applying the correlative model to the process data set to qualitatively and/or quantitatively predict a value of a property and/or composition of the target analyte.

An apparatus for determining a characteristic of a photoluminescent (PL) layer comprises: a light source that generates an excitation light that includes light from the visible or near-visible spectrum; an optical assembly configured to direct the excitation light onto a PL layer; a detector that is configured to receive a PL emission generated by the PL layer in response to the excitation light interacting with the PL layer and generate a signal based on the PL emission; and a computing device coupled to the detector and configured to receive the signal from the detector and determine a characteristic of the PL layer based on the signal.

An apparatus (1) for inspecting an object, where the object is made up of a first layer of plastic material and a second layer of EVO or EVOH and has a base wall (A) and a side wall (B) which is inclined relative to the base wall (A), comprises: an inspection zone (10) in which the object can be placed for inspection; a conveyor (12) for feeding the object to the inspection zone (10) along a feed plane (P); an imaging device (14) configured to view the object positioned in the inspection zone (10) and to generate an image (143) of the object; a processor (151), configured to process the image (143), to inspect the second layer. The conveyor (12) is configured to dispose the object in the inspection zone (10) with the base wall (A) positioned according to a predetermined orientation relative to the feed plane (P).

An automated method of inspecting a pipe includes: positioning the pipe with respect to a laser scanner using a positioning apparatus; scanning a size of the positioned pipe by the laser scanner; identifying a specification and historical data of the pipe's type by inputting the scanned size to an artificially intelligent module trained through machine learning to match input size data to standardized pipe types and output corresponding specifications and historical data of the pipe types; scanning dimensions of the positioned pipe by the laser scanner using a dimension portion of the identified historical data; comparing the scanned dimensions with standard dimensions from the identified specification; detecting a dimension nonconformity when the scanned dimensions are not within acceptable tolerances of the standard dimensions; and in response to detecting the dimension nonconformity, generating an alert and updating the dimension portion of the identified historical data to reflect the detected dimension nonconformity.

Provided is a method for the simultaneous determination of parameters, in particular of at least two, three or four parameters, of at least one resin layer applied to at least one carrier material by recording and evaluating at least one NIR spectrum in a wavelength range between 500 nm and 2500 nm, preferably between 700 nm and 2000 nm, more preferably between 900 nm and 1700 nm, and particularly advantageously between 1450 nm and 1550 nm, using at least one NIR measuring head, in particular at least one NIR multimeter head.

The monitoring and control of bioprocesses is provided. The present disclosure provides the ability to generate generic calibration models, independent of cell line, using inline Raman probes to monitor changes in glucose, lactate, glutamate, ammonium, viable cell concentration (VCC), total cell concentration (TCC) and product concentration. Calibration models were developed from cell culture using two different CHOK1SV GS-KO™ cell lines producing different monoclonal antibodies (mAbs). Developed predictive models, qualified using an independent CHOK1SV GS-KO™ cell line not used in calibration, measured changes in glucose, lactate, ammonium, VCC, and TCC with minor prediction errors over the course of cell culture with minimal cell line dependence. The development of these generic models allows the application of spectroscopic PAT techniques in a clinical manufacturing environment, where processes are typically run once or twice in GMP manufacturing based on a common platform process.

In some implementations, a device may identify, based on spectroscopic data, a pseudo steady state end point indicating an end of a pseudo steady state associated with the blending process. The device may identify a reference block and a test block from the spectroscopic data based on the pseudo steady state end point. The device may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate a statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block. The device may determine whether the blending process has reached a steady state based on the statistical detection signal.

A device may receive spectroscopic data associated with a dynamic process. The device may identify a pseudo steady state end point based on the spectroscopic data. The pseudo steady state end point may indicate an end of a pseudo steady state associated with the dynamic process. The device may identify a reference block and a test block based on the pseudo steady state end point, and may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate an averaged statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block, and may determine whether the dynamic process has reached a steady state based on the averaged statistical detection signal.