An example printing device includes: a paper path; a component controllable to cause reduction of moisture along the paper path; a sensor and a reflective surface on opposite sides of the paper path such that paper moves along the paper path between the sensor and the reflective surface, the sensor to: emit light towards the reflective surface; and detect light reflected from the reflective surface; and a processor connected to the sensor and a memory storing instructions, the processor to execute the instructions to cause the processor to: communicate with the sensor to determine that the light detected by the sensor, as reflected from the reflective surface, meets a moisture threshold condition; and in response to determining that the light detected by the sensor meets the moisture threshold condition, control the component to reduce the moisture along the paper path.

An example printing device includes: a paper path; a component controllable to cause reduction of moisture along the paper path; a sensor and a reflective surface on opposite sides of the paper path such that paper moves along the paper path between the sensor and the reflective surface, the sensor to: emit light towards the reflective surface; and detect light reflected from the reflective surface; and a processor connected to the sensor and a memory storing instructions, the processor to execute the instructions to cause the processor to: communicate with the sensor to determine that the light detected by the sensor, as reflected from the reflective surface, meets a moisture threshold condition; and in response to determining that the light detected by the sensor meets the moisture threshold condition, control the component to reduce the moisture along the paper path.

The quality measurement method for a long sheet material W includes measuring cellulose fibers, % moisture, and % ash of the paper web W by using area cameras 1102 to 1106 having an infrared light receiving element and a light source 1100 having an infrared light emitting LED element. Performance check for the infrared cameras 1102 to 1106 over the entire width and correction of measured values are performed by using consistency between measured values for the same point in an overlap area measured by adjacent cameras and reference samples 1107 at the off-sheet positions provided at both sides.

The quality measurement method for a long sheet material W includes measuring cellulose fibers, % moisture, and % ash of the paper web W by using area cameras 1102 to 1106 having an infrared light receiving element and a light source 1100 having an infrared light emitting LED element. Performance check for the infrared cameras 1102 to 1106 over the entire width and correction of measured values are performed by using consistency between measured values for the same point in an overlap area measured by adjacent cameras and reference samples 1107 at the off-sheet positions provided at both sides.

A moisture detecting apparatus includes: a light emitting unit including a first light source configured to emit light having a first wavelength as a peak wavelength, and a second light source configured to emit light having a second wavelength as a peak wavelength; a detecting unit configured to detect a first detection value indicating an extent to which the light emitted from the first light source is transmitted through a recording material, and a second detection value indicating an extent to which the light emitted by the second light source is transmitted through the recording material, based on a light receiving result of a light receiving unit; and a determination unit configured to determine a value related to a moisture content of the recording material based on the first detection value and the second detection value.

A moisture detecting apparatus includes: a light emitting unit including a first light source configured to emit light having a first wavelength as a peak wavelength, and a second light source configured to emit light having a second wavelength as a peak wavelength; a detecting unit configured to detect a first detection value indicating an extent to which the light emitted from the first light source is transmitted through a recording material, and a second detection value indicating an extent to which the light emitted by the second light source is transmitted through the recording material, based on a light receiving result of a light receiving unit; and a determination unit configured to determine a value related to a moisture content of the recording material based on the first detection value and the second detection value.

A moisture sensor detects moisture in an object. A first light-emitting element emits light. A first reflecting member reflects light reflected from the object toward the object. A light-receiving element receives light that is multiple reflected between the object and the first reflecting member. A controller determines an amount of moisture in the object based on a light reception result from the light-receiving element.

A moisture sensor detects moisture in an object. A first light-emitting element emits light. A first reflecting member reflects light reflected from the object toward the object. A light-receiving element receives light that is multiple reflected between the object and the first reflecting member. A controller determines an amount of moisture in the object based on a light reception result from the light-receiving element.

Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.