Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R² = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.

The field of applied spectroscopy is strongly dominated by publications presenting proof-of-concepts, lab set-ups, and demonstrations. In contrast, the corresponding number of commercial successes of inline spectroscopy is surprisingly lower. This article discusses inline spectroscopy from an instrumentation perspective. It is the authors' firm belief that the success of inline spectroscopy lies in the understanding of how the design and implementation of the optical instrumentation affects the data quality, and how this in turn will limit or enhance the performance of the prediction model. This article emphasizes the need for a strong, multidisciplinary design team, whose design process is rooted in first principles, to bridge the technology “valley of death” and convert research in applied spectroscopy into commercially successful solutions.

Autonomous field robots are being developed for picking of fruit, where each fruit needs to be individually graded and handled. There is therefore a need for rapid and non-destructive sensing to measure critical fruit quality parameters. In this article we report how total soluble solids (TSS), a measure for total sugar content, can be measured in strawberries in the field by non-contact near-infrared (NIR) interaction spectroscopy. A specially designed prototype system working in the wavelength range 760-1080 nm was tested for this purpose. This novel instrument was compared with a commercial handheld NIR reflection instrument working in the range 900-1600 nm. The instruments were calibrated in the lab using data collected from 200 strawberries of two varieties and tested in a strawberry field on 50 berries in 2022 and 100 berries in 2023. Both systems performed well during calibration with root mean square errors of cross validation for TSS around 0.49 % and 0.57 %, for interaction and reflection, respectively. For prediction of TSS in new berries in 2023, the interaction system was superior, with a prediction error of 1.0 % versus 8.1 % for the reflection system, most likely because interaction probes deeper into the berries. The results suggest that interaction measurements of average TSS are more robust and would most likely require less calibration maintenance compared to reflection measurements. The non-contact feature is important since it reduces the spread of diseases and physical damage to the berries.

This article investigates the possibility of using non-contact interactance as a method for profiling the temperature in a processed meat product (liver pâté) as it comes out of the oven. The application was defined by an industrial partner, Nortura SA, Tønsberg, Norway, where more control of the cooking process was desired. The optical system employs low spectral resolution to achieve high enough signal-to-noise ratio (SNR) to depths of 2 cm into the product. The partial least squares (PLS) method was applied to interactance spectra in the region 760–1040 nm and a root mean square error of 1.52 °C was obtained. The model was tested on five different validation sets spread over 18 months and a root mean square error of prediction of 2.66 °C was achieved. The output of this model was based on the weighted average of two temperatures in the first 2 cm of the liver pâté, one of which is the core temperature. A comparison was also made with two other models: a model based on the core temperature alone and a model based again on the weighted temperature but using the shorter wavelength range of 905.5–1047 nm. These two models gave less favorable prediction errors.

A porcine model was used to investigate the feasibility of using VIS-NIR spectroscopy to differentiate between degrees of ischemia–reperfusion injury in the small intestine. Ten pigs were used in this study and four segments were created in the small intestine of each pig: (1) control, (2) full arterial and venous mesenteric occlusion for 8 h, (3) arterial and venous mesenteric occlusion for 2 h followed by reperfusion for 6 h, and (4) arterial and venous mesenteric occlusion for 4 h followed by reperfusion for 4 h. Two models were built using partial least square discriminant analysis. The first model was able to differentiate between the control, ischemic, and reperfused intestinal segments with an average accuracy of 99.2% with 10-fold cross-validation, and the second model was able to discriminate between the viable versus non-viable intestinal segments with an average accuracy of 96.0% using 10-fold cross-validation. Moreover, histopathology was used to investigate the borderline between viable and non-viable intestinal segments. The VIS-NIR spectroscopy method together with a PLS-DA model showed promising results and appears to be well-suited as a potentially real-time intraoperative method for assessing intestinal ischemia–reperfusion injury, due to its easy-to-use and non-invasive nature.

Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.