Background The increasing demand for quality assurance in agro-food production requires sophisticated analytical methods for in-line quality control. One of these techniques is visible and near-infrared (VIS-NIR) spectroscopy, which has low running costs, does not need sample preparation, and is non-destructive, environmentally friendly, and fast. Despite these advantages, only a limited amount of research has been conducted on VIS-NIR in-line applications to measure, control, and predict quality in fruits and vegetables. Scope and Approach The applicability of VIS-NIR spectroscopy for the off-line and in-line monitoring of quality in postharvest products has been addressed in this review. The document focuses on the comparison between the two processes for the same agro-food product, highlighting the main advantages and disadvantages, problems, solutions, and differences. Key Findings and Conclusions VIS-NIR techniques, combined with chemometric methods, have shown great potential due to their fast detection speed, and the possibility of simultaneously predicting multiple quality parameters or distinguishing between products according to the objectives. Being able to automate processes is a great advantage compared to routine off-line analyses, mainly due to the savings achieved in time, material, and personnel. However, in numerous cases, in-line implementation has not been accomplished in the corresponding studies, hence the scarcity of real in-line applications. Recent demands, together with the advances being made in the technology and a reduction in the price of equipment, makes VIS-NIR technology an analytical alternative for continuous realtime food quality controls, which will become predominant in the next few years.

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Early control of fruit quality requires reliable and rapid determination techniques. Therefore, the food industry has a growing interest in non-destructive methods such as spectroscopy. The aim of this study was to evaluate the feasibility of visible and nearinfrared (NIR) spectroscopy, in combination with multivariate analysis techniques, to predict the level and changes of astringency in intact and in the flesh of half cut persimmon fruits. The fruits were harvested and exposed to different treatments with 95 % CO 2 at 20 ºC for 0, 6, 12, 18 and 24 h to obtain samples with different levels of astringency. A set of 98 fruits was used to develop the predictive models based on their spectral data and another external set of 42 fruit samples was used to validate the models. The models were created using the partial least squares regression (PLSR), support vector machine (SVM) and least squares support vector machine (LS-SVM). In general, the models with the best performance were those which included standard normal variate (SNV) in the pre-processing. The best model was the PLSR developed with SNV along with the first derivative (1-Der) pre-processing, created using the data obtained at six measurement points of the intact fruits and all wavelengths (R 2 =0.904 and RPD=3.26). Later, a successive projection algorithm (SPA) was applied to select the most effective wavelengths (EWs). Using the six points of measurement of the *Manuscript Click here to view linked References intact fruit and SNV together with the direct orthogonal signal correction (DOSC) preprocessing in the NIR spectra, 41 EWs were selected, achieving an R 2 of 0.915 and an RPD of 3.46 for the PLSR model. These results suggest that this technology has potential for use as a feasible and cost-effective method for the non-destructive determination of astringency in persimmon fruits.

Objective: To analyze those factors contributing to the diagnostic delay in ALS.Methods: Consecutive ALS patients were categorized as those studied in departmental hospitals and those studied in a referral ALS center. Demographic and clinical variables, together with data of the diagnostic pathway were collected. Multivariable models were used to assess their effect in the time between symptoms onset and the first neurologist visit (time symptoms-neurologist), in the time between the first neurologist visit and the diagnosis (time neurologist-diagnosis) and in the diagnostic delay.Results: 166 ALS patients with a median diagnostic delay of 11.53 months (IQR: 6.68, 15.23) were included. The median diagnostic delay was 8.57 months (5.16, 11.61) in the referral center vs. 12.08 months (6.87, 16.8) in departmental centers. Bulbar onset, fast progression rate, upper motor neuron predominant phenotype and an early referral to the neurologist were associated with a shorter time between symptoms–neurologist. Being studied in a referral center was associated with a shorter time between neurologist–diagnosis. Comorbidities, familial ALS, bulbar onset, early referral to the neurologist and being studied in a referral center were associated with a shorter diagnostic delay. For patients studied in departmental hospitals, fast progression rate was also strongly associated with a shorter time between neurologist–diagnosis and diagnostic delay.Conclusion: Unmodifiable factors (comorbidities, familial ALS, bulbar onset, and progression rate) as well as modifiable factors (early referral to the neurologist and the evaluation in an ALS referral center) have an independent effect in the diagnostic delay. The universalization of ALS Units is probably the most efficient measure to reduce the diagnostic delay.