The influence of different data pre-processing methods (smoothing by moving average (MA),
multiplicative scatter correction (MSC), Savitzky-Golay (SG), standard normal variate (SNV)
and mean normalization (MN) on the prediction of sugar content from sugarcane samples was
investigated. The performance of these pre-processing methods was evaluated using spectral
data collected from 292 sugarcane internode samples using a visible-shortwave near infrared
spectroradiometer (VNIRS). Partial least square (PLS) method was applied to develop both
calibration and prediction models for the samples. If no pre-processing method was applied,
the coefficient of determination (R2) values for both reflectance and absorbance data were 0.81
and 0.86 respectively. The highest prediction accuracy values were obtained when the data was
treated with MSC method, where the R2 values for reflectance and absorbance being 0.85 and
0.87, respectively. From this study, it was concluded that pre-processing can improve the model
performances where MSC method was found to give the highest prediction accuracy value.
The COVID-19 pandemic has drastically altered the education sector. Rather than the impact of COVID-19, many higher education institutions (HEIs) are on the verge of insolvency due to a lack of digital transformation readiness and poor business models. The bleak financial future many HEIs will face while others may be forced to close their doors completely will erode HEIs' ability to fulfil their societal responsibilities. However, HEIs that have survived and maintained their operations anticipate the transition to online learning or the effects of any economic crisis, including university closures in the short, medium, or long term. The entire educational ecosystem was forced to transform its operations quickly and entirely to an online teaching-learning scenario in just a few weeks. Notably, HEIs that have long offered online courses worldwide can easily transition to digital teaching and learning when necessary. The second roundtable session's result of the International Higher Education Conference, organized by INTI International University on March 31 2022, was used to organize a Delphi method to identify further factors that positively impact HEIs by COVID-19. The importance of these factors was then determined using Kendall's coefficient of concordance. Recommendations on how HEIs should move towards institutional sustainability during the endemic phase are presented accordingly.